Harvard University

APMTH 231: Decision Theory (FAS, Applied Mathematics, Spring) Instructor(s): Demba Ba. This course focuses on statistical inference and estimation from a signal processing perspective. The course will emphasize the entire pipeline from writing a model, estimating its parameters and performing inference utilizing real data. The first part of the course will focus on linear and nonlinear probabilistic generative/regression models (e.g., linear, logistic, Poisson regression), and algorithms for optimization (ML/MAP estimation) and Bayesian inference in these models. We will pay particular attention to sparsity-induced regression models, because of their relation to artificial neural networks, the topic of the second part of the course. The second part of the course will introduce students to the nascent and exciting research area of model-based deep learning and sparse auto-encoders. We will see, for instance, how neural-networks with ReLU nonlinearities arise from sparse probabilistic generative models introduced in the first part of the course. This will form the basis for a rigorous recipe we will teach you to build interpretable deep neural networks, from the ground up. More broadly, model-based deep learning and sparse auto-encoders have become popular approaches to reverse-engineer intelligence in both biological and artificial settings: in each case, we are able to train these systems to perform complicated tasks, but our understanding of how they do so remains opaque. Reverse engineering intelligence—whether in the brain or in artificial neural networks—means using mathematical tools to reveal what information these systems truly represent. By moving beyond performance metrics to probe internal representations, we gain interpretability and transparency, with real-world benefits for the safety, fairness, and trustworthiness of modern AI and brain-machine systems. We will invite an exciting lineup of speakers. We encourage you to pursue a final project that could lead to prototype or production solutions to challenges businesses face around AI adoption due to lack of transparency. Course ID: 203548

ECON 2059: Decision Theory (FAS, Economics, Fall) Instructor(s): Tomasz Strzalecki. This course prepares students for pure and applied research in axiomatic decision theory. We start with a rigorous treatment of the classical topics that are at the heart of all of economics (utility maximization, expected utility, discounted utility, Bayesian updating, dynamic consistency, option value). We then delve into a number of modern topics inspired by the observed violations of the classical models (“exotic preferences” used in macro-finance, ambiguity aversion, temptation and self-control). The last part of the course explores the recently flourishing literature on stochastic choice (which is related to, but distinct from, discrete choice econometrics). Prerequisites/Notes: Prerequisites include basic microeconomic theory at the level of Mas Colell, Whinston, Green; being comfortable with abstract models. Course ID: 121331

RDS 284: Decision Theory (HSPH, Fall) Instructor(s): James Hammitt. Introduces the standard model of decision-making under uncertainty, its conceptual foundations, challenges, alternatives, and methodological issues arising from the application of these techniques to health issues. Topics include von Neumann-Morgenstern and multi-attribute utility theory, Bayesian statistical decision theory, stochastic dominance, the value of information, judgment under uncertainty and alternative models of probability and decision making (regret theory, prospect theory, generalized expected utility). Applications are to preferences for health and aggregation of preferences over time and across individuals. Course ID: 191105

API 302 / ECON 1415: Analytic Frameworks for Policy (HKS, Economics, Fall) Instructor(s): Richard Zeckhauser. This course develops abilities in using analytic frameworks in the formulation and assessment of public policies. It considers a variety of analytic techniques, particularly those directed toward uncertainty and interactive decision problems. It emphasizes the application of techniques to policy analysis, not formal derivations. Students encounter case studies, methodological readings, modeling of current events, the computer, a final exam, and challenging problem sets. Course IDs: 170053, 107613

GHP 228: Econometric Methods in Impact Evaluation (HSPH, Spring) Instructor(s): Jessica Cohen. The objective of this course is to provide students with a set of theoretical, econometric and reasoning skills to estimate the causal impact of one variable on another. Examples from the readings explore the causal effect of policies, laws, programs and natural experiments. We will go beyond estimating causal effects to analyze the channels through which the causal impact was likely achieved. The course will introduce students to a variety of econometric techniques in impact evaluation and a set of reasoning skills intended to help them become both a consumer and producer of applied empirical research. Students will learn to critically analyze evaluation research and to gauge how convincing the research is in identifying a causal impact. They will use these skills to develop an evaluation plan for a topic of their own, with the aim of stimulating ideas for dissertation research. This is a methods class that relies heavily on familiarity with regression analysis and econometrics. Prerequisites/Notes: Coursework in econometrics is a prerequisite for the course without exception. The course is intended for doctoral students who are finishing their course work and aims to help them transition into independent research. The aim of this course is to prepare doctoral students for the dissertation phase of their research and thus they will be given priority in enrollment. The course is also open to master’s students, conditional on having adequate training and the course having enough space. Coursework in econometrics is required and some coursework in economics is beneficial but not strictly required. Some previous experience with regression analysis and applied economic research will be a huge advantage. Students seeing applied regression analysis for the first time in this course will most likely struggle with the reading.  Students interested in taking this course must request instructor permission. Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 190392

RDS 202: Decision Science for Public Health (HSPH, Spring) Instructor(s): Sue J. Goldie and Eve Wittenberg. Challenges in public health policy and clinical medicine are marked by complexity, uncertainty, competing priorities and resource constraints. This course is designed to introduce the student to the methods and applications of decision analysis and cost-effectiveness analysis in clinical and public health decision making. The objectives of the course are: (1) to provide a basic introduction to the methods and tools of decision science and to recognize when, how, and in what context they can provide value in clinical and public health decision making; (2) to equip students with the ability to structure and bound a decision problem logically (articulating the objective, perspective, and time horizon) and to identify key elements (alternatives, uncertainties, and outcomes) and influential factors (preferences, risk attitudes, values); (3) to provide students with basic skills in revising probabilities given new information, building and analyzing decision trees, and conducting a cost-effectiveness analysis; (4) to enable students to thoughtfully and critically evaluate published analyses conducted to evaluate or inform clinical strategies, health technologies, and public health policies in developed and developing countries. Prerequisites/Notes: This course serves as a prerequisite for RDS 285 and RDS 288. Students cannot take RDS 202 if they have already taken RDS 280 or RDS 286 (exceptions only allowed with permission of RDS 202 instructor). Course ID: 204407

RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor(s): Ankur Pandya. This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in high-income and low-income settings. Prerequisites/Notes: Prerequisites are ID 201 or BIO200 or BST 201 or BST 202& BST 203 or BST 206 & BST 209 (all courses may be taken concurrently). Students who have taken RDS 286 may not take RDS 280. Prerequisites can be waived with RDS 280 instructor’s permission. Introductory economics is recommended but not required. Course ID: 191102

RDS 282: Economic Evaluation of Health Policy & Program Management (HSPH, Spring) Instructor(s): Ankur Pandya. This course features the application of health decision science to policymaking and program management at various levels of the health system. Both developed and developing country contexts will be covered. Topics include: [1] theoretical foundations of cost-effectiveness analysis (CEA) with comparison to other methods of economic evaluation; [2] challenges and critiques of CEA in practice; [3] design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs; [4] use of evidence of economic value in strategic planning and resource allocation decisions, performance monitoring and program evaluation; [5] the role of evidence of economic value in the context of other stakeholder criteria and political motivations. Prerequisites/Notes: Students must have taken RDS 280 or RDS 286. Concurrent enrollment is allowed. Prior coursework in Microeconomics is recommended. Course ID: 191104

RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructor(s): Nicolas Menzies. This intermediate-level course focuses on methods and health applications of decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis. Prerequisites/Notes: Prerequisites include (BST201 or ID201 or (BST 206 and BST 207)) and (RDS280 or RDS286 or RDS 202). Concurrent enrollment is allowed for RDS 286. Concurrent enrollment is not allowed for RDS 202. Familiarity with matrix algebra and elementary calculus may be helpful but not required; lab or section times to be announced at first meeting. Course ID: 191106

RDS 286: Decision Analysis in Clinical Research (HSPH, Summer) Instructor(s): Uwe Siebert. This course introduces students to the systematic methods of evidence-based decision analysis and cost-effectiveness analysis, and familiarizes students with their growing range of applications in clinical and public health decision making, clinical and comparative effectiveness research, economic evaluation and health technology assessment. Topics of the sessions include: the use of causal trees/diagrams/estimands to express comparative efficacy and real-world effectiveness for clinical procedures and public health interventions; the principles of linked evidence and different model types; the use of sensitivity analysis to assess and express uncertainty; Bayes theorem and evaluation of diagnostic test strategies; utility theory and its use to integrate patient preferences in health outcomes; benefit-harm analysis and cost-effectiveness analysis for clinical research, clinical guideline development, health technology assessment and health policy decision making. Lectures are accompanied by case problems, as well as optional group work breakout sessions, self-assessment quizzes, review sessions and computer exercises. After this course, students will understand the uses, strengths, limitations and ethical issues of decision analysis and cost effectiveness in clinical decision making and research design. We will discuss case examples for prevention, treatment and disease management from different disease areas including cancer, cardiovascular disease, infectious disease, pandemic control, and others. Prerequisites/Notes: Requires prior knowledge of clinical medicine or related subjects (through training and/or clinical research experience) and strong quantitative ability/aptitude. Priority for enrollment will be given to students in the Program for Clinical Effectiveness (PCE). Course ID: 191107

RDS 290: Experiential Learning and Applied Research in Decision Analysis (HSPH, Spring) Instructor(s): Ankur Pandya. This course is geared towards Masters-level students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students may work alone or in pairs, including at least one student who is familiar with the clinical content area of the project. Prerequisites/Notes: Prerequisites include (RDS 280 or 286 or 202, any of which cannot be taken concurrently to satisfy the prerequisite) and (RDS 285, which can be taken concurrently to satisfy the prerequisite, or 288). Course ID: 206897

RDS 500: Risk Assessment (HSPH, Fall) Instructor(s): David Macintosh. This course introduces the framework of risk assessment, considers its relationship with cost-benefit, decision analysis and other tools for improving environmental decisions. The scientific foundations for risk assessment (epidemiology, toxicology, and exposure assessment) are discussed. The mathematical sciences involved in developing models of dose-response, fate and transport, and the statistical aspects of parameter estimation and uncertainty analysis are introduced. Case studies are used to illustrate various issues in risk assessment and decision making. Course activities include lectures, discussions, and case studies. Prerequisites/Notes: Course required for all Exposure, Epidemiology and Risk Program students.. Course ID: 191111

ECON 2020B: Microeconomic Theory II (FAS, Spring) Instructor(s): Christopher Avery. A continuation of Economics 2020a. Topics include game theory, economics of information, incentive theory, and welfare economics. Course ID: 113615

GHP 230: Introduction to Economics with Applications to Health and Development (HSPH, Fall) Instructor(s): James Potter. This course provides an overview of the microeconomic theories and concepts most relevant for understanding health and development. Each week of the course will cover basic concepts in economics with an application to health. It describes how the markets for health and health services are different from other goods, with a particular emphasis on the role of government and market failure. In addition it discusses the theoretical and empirical aspects of key health economics issues, including the demand for health and health services, supply side concerns, health insurance, the provision of public goods, and related topics. The course encourages students to fundamentally and rigorously examine the role of the market for the provision of health and health services and how public policy can influence these markets. At the completion of the course, you will: (1) Understand the basic intuition of microeconomics models of consumers, producers and welfare, (2) Understand market failures, their implications and solutions, (3) Be familiar with current issues in global health economics around the demand for health and health insurance, and (4) Consume, discuss and write about economic studies of health and health care systems. Prerequisites/Notes: Course is required for GHP-SM2, MPH45-GH. Any remaining seats will be available on a first-come first-serve basis. Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 190394

ID 250: Ethical Basis of the Practice of Public Health (HSPH, Fall) Instructor(s): Ole Norheim. This course serves as an introduction to ethical issues in the practice of public health. Students will identify a number of key ethical issues and dilemmas arising in efforts to improve and protect population health and will become familiar with the principal arguments and evidence supporting contesting views. The class aims to enhance the students’ capacity for using ethical reasoning in resolving the ethical issues that will arise throughout their careers. Unlike courses in medical ethics, which mainly examine ethical dilemmas facing individual clinicians, the population-level focus of this course directs our attention to questions of ethics and justice that must be addressed at the societal level. These include: What social response is required of a just society to the needs of its members for protecting and restoring health? Is population health something other than the aggregate of the health concerns of the individuals who make up a society at a given time? And what are the ethical implications of the answers? When are inequalities in health inequitable, and what priority should be assigned to reducing disparities in health when pursuing this goal might compromise the effort to maximize population health? Which ethical choices, if any, are unavoidable in developing the methodologies for measurement of health and of the global burden of disease? Which ethical choices if any are unavoidable in developing and using methods for priority-setting such as cost-effectiveness analysis and cost-benefit analysis? Are the ethical commitments of the profession of public health consistent with some methods and not others? Should the institution of universal health coverage be guided by ethical precepts and if so, what are these values and how should they guide policy? Can and should public health’s dedication to improving population health conflict with the priorities of some individuals whose choices to not reflect such high priority for health? Should these individual preferences always be respected? Are there effective strategies that pursue population health in the face of such conflicts while preserving the individual’s freedom to make unhealthy choices? How should responsibility for poor health be assigned, and what are the ethical implications of this assignment for poor health due to health problems due to smoking, obesity, and other unhealthy behaviors? To the extent that the socioeconomic health gradient reflects differences in how well people take care of themselves, are these disparities in health individual failings rather than social injustices? Course ID: 190768

APMTH 115 / ENG-SCI 115: Mathematical Modeling (SEAS, Fall; FAS, Spring) Instructor(s): Michael P. Brenner, Zhiming Kuang. Abstracting the essential components and mechanisms from a natural system to produce a mathematical model, which can be analyzed with a variety of formal mathematical methods, is perhaps the most important, but least understood, task in applied mathematics. This course approaches a number of problems without the prejudice of trying to apply a particular method of solution. Topics drawn from biology, economics, engineering, physical and social sciences. Course ID: 118021, 156427

E-PSCI 236: Environmental Modeling and Data Analysis (FAS, Earth & Planetary Sciences, Fall) Instructor(s): Steven Wofsy. This course introduces environmental modeling and data analysis: data visualization, statistical inference, Bayes Theorem, optimal estimation, adjoint methods, Monte Carlo methods, time series analysis, denoising; principles and numerical methods for chemical transport and inverse models. Course ID: 120783

EPI 260: Mathematical Modeling of Infectious Diseases (HSPH, Spring) Instructor(s): Jeff Imai-Eaton. This course covers selected topics and techniques in the use of dynamical models to study the transmission dynamics of infectious diseases. Class sessions will primarily consist of lectures and demonstrations of modeling techniques. Techniques include design and construction of appropriate differential equation models, equilibrium and stability analysis, parameter estimation from epidemiological data, determination and interpretation of the basic reproductive number of an infection, techniques for sensitivity analysis, and critique of model assumptions. Specific topics include the use of age-seroprevalence data, the effects of population heterogeneity on transmission, stochastic models, and the use of models for pathogens with multiple strains. This course is designed for students with a basic understanding of mathematical modeling concepts who want to develop models for their own work. Prerequisites/Notes: EPI 501; may be taken concurrently. Previous course in calculus is required. Course ID: 190321

EPI 501: Dynamics of Infectious Diseases (HSPH, Spring) Instructor(s): Megan Murray. This course covers the basic concepts of infectious disease dynamics within human populations. Focus will be on transmission of infectious agents and the effect of biological, ecological, social, political, economic forces on the spread of infections. We will emphasize the impact of vaccination programs and other interventions. The dynamics of host-parasite interaction are illustrated using basic mathematical modeling techniques. A key component of the course is the introduction to the programming mathematical modeling techniques. A key component of the course is the introduction to the programming language R, which we will use for all mathematical modeling activities and examples.Course Activities include in-class demonstrations and practical sessions, written homework assignments and final class debate.  Prerequisites/Notes: Previous coursework in epidemiology and programming helpful but not required. Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 10172

GHP 201: Advanced Modeling for Health System Analysis & Priority Setting (HSPH, Spring) Instructor(s): Stéphane Verguet. This course directly builds on GHP 501, and offers advanced methods for modeling for health system analysis and priority setting in global health. Students will apply a range of techniques to address central topics, including: health disparities; medical impoverishment and financial risk protection; economic evaluations for health policy assessment; health system modeling; health system performance and country performance on health. Through readings, basic programming using R software (www.r-project.org), and research projects, students will develop their research skills around three main areas of application, with an emphasis on low- and middle-income countries: I. Economic evaluation for health policy assessment; II. Health system modeling; III. Efficiency, equity, and performance. Prerequisites/Notes: GHP501 is a prerequisite. Instructor permission is required for enrollment. Students who wish to enroll must request instructor permission in my.Harvard. Course ID: 207842

GHP 501: Modeling for Health System Analysis & Priority Setting (HSPH, Spring) Instructor(s) Stéphane Verguet. This course offers an introduction to modeling for health system analysis and priority setting in global health, and its key quantitative methods. Students will learn to use a range of tools to address central concerns and topics, including: health disparities; medical impoverishment and financial risk protection; economic evaluations for health policy assessment; health system performance and country performance on health. Modeling for health system analysis – and therefore this course – draws from the disciplines of global public health, health services research, epidemiology, economics and applied mathematics. Through readings, homework, basic programming using R software (www.r-project.org), and a research assignment, students will gain solid quantitative knowledge of the field. The course is designed around three main areas of inquiry and application, with an emphasis on low- and middle-income countries: I. Economic evaluation for health policy assessment; II. Health system modeling; III. Efficiency, equity, and performance. Prerequisites/Notes: Instructor permission is required for enrollment. Students who wish to enroll must request instructor permission in my.Harvard. Course ID: 204258

RDS 203: Advanced Computational Methods for Disease Modelling (HSPH, Spring) Instructor(s): Zachary Ward. This advanced course covers statistical and computational methods applicable to disease modelling in public health and medicine. Students will learn to apply state-of-the-art methods related to three core modules: 1) Numerical Methods, 2) Simulation-based Inference, and 3) High Performance Computing. Prerequisites/Notes: Prerequisites include a course in mathematical modeling (RDS 285 or RDS 288), probability and statistics (BST 201 or ID 201 or (BST 206 and BST 207)), and basic knowledge of mathematical notation and reasoning. Prior programming experience (e.g., R, Python, C++, Java) is strongly recommended. Course ID: 224659

RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor(s): Ankur Pandya. This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in high-income and low-income settings. Prerequisites/Notes: Prerequisites include ID 201 or BIO200 or BST201 or BST202&203 or BST206&209 (all courses may be taken concurrently). Students who have taken RDS 286 may not take RDS 280. Prerequisites can be waived with RDS 280 instructor’s permission. Introductory economics is recommended but not required. Course ID: 191102

RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructor(s): Nicolas Menzies. This intermediate-level course focuses on methods and health applications of decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis. Prerequisites/Notes: Prerequisites include (BST201 or ID201 or (BST 206 and BST 207)) and (RDS280 or RDS286 or RDS 202). Concurrent enrollment is allowed for RDS 286. Concurrent enrollment is not allowed for RDS 202. Familiarity with matrix algebra and elementary calculus may be helpful but not required; lab or section times to be announced at first meeting. Course ID: 191106

RDS 290: Experiential Learning and Applied Research in Decision Analysis (HSPH, Spring) Instructor(s): Ankur Pandya. This course is geared towards Master’s-level students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students may work alone or in pairs, including at least one student who is familiar with the clinical content area of the project. Prerequisites/Notes: Prerequisites include (RDS 280 or 286 or 202, any of which cannot be taken concurrently to satisfy the prerequisite) and (RDS 285, which can be taken concurrently to satisfy the prerequisite, or 288). Course ID: 206897

API 222: Machine Learning and Big Data Analytics (HKS, Fall) Instructor(s): To be announced. In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems. We will cover various recent techniques and their applications from supervised, unsupervised, and reinforcement learning. In addition, students will get the chance to work with some data sets using software and apply their knowledge to a variety of examples from a broad array of industries and policy domains. Some of the intended course topics (time permitting) include: K-Nearest Neighbors, Naive Bayes, Logistic Regression, Linear and Quadratic Discriminant Analysis, Model Selection (Cross Validation, Bootstrapping), Support Vector Machines, Smoothing Splines, Generalized Additive Models, Shrinkage Methods (Lasso, Ridge), Dimension Reduction Methods (Principal Component Regression, Partial Least Squares), Decision Trees, Bagging, Boosting, Random Forest, K-Means Clustering, Hierarchical Clustering, Neural Networks, Deep Learning, and Reinforcement Learning. Course ID: 208037

ENG-SCI 121/MTH 121: Introduction to Optimization: Models and Methods (FAS, Fall) Instructor(s): Melanie Weber. This course provides an introduction to basic mathematical ideas and computational methods for optimization. Topics include linear programming, integer programming, branch-and-bound, branch-and-cut, as well as first-order gradient-based methods with an emphasis on modeling and data science applications. Course ID: 156288, 11858

HPM 732: Operations Management in Service Delivery Organizations (HSPH, Summer) Instructor(s): Joseph Pliskin. This course introduces concepts of operations management in service delivery organizations: operations management is concerned with evaluating the performance of operating units, understanding why they perform as they do, designing new or improved operating procedures and systems for competitive advantage, making short-run and long-run decisions that affect operations, and managing the work force. To understand the role of operations in any organization, a manager must understand process analysis, capacity analysis, types of processes, productivity analysis, development and use of quality standards, and the role of operating strategy in corporate strategy. The course introduces students to these concepts and will also present the focused management approach which can help an organization achieve more with existing resources. Similar to HPM 232 – adapted for the non-residential program. Prerequisites/Notes: Open only to students in Master in Health Care Management Program. Course ID: 10065

MLD 601: Operations Management (HKS, Fall) Instructor(s): Mark Fagan. This course is an introduction to operations management which entails creating public value by efficiently delivering quality services. The course provides students with the tools to identify opportunities for improvement, diagnose problems and barriers, and design efficient and effective solutions. The course uses the case method of instruction, drawing examples primarily from the public and nonprofit sectors with some private sector cases. The course roadmap is: creating value, delivering quality services, delivering efficient services, managing performance, utilizing technology, and addressing unique challenges. Throughout the course, tools will be introduced including process mapping and reengineering, capacity and root-cause analysis, and total quality management. The course capstone is a client project in which student teams help local agencies solve actual operational problems. A Friday recitation provides additional practice with the tools that are taught. Prerequisites/Notes: The course is oriented toward the general manager or those interested in an introduction to the field. Course ID: 170531

ECON 2030: Psychology and Economics (FAS, Economics, Spring) Instructor(s): David Laibson. Studies the way that economic and psychological factors jointly influence behavior. Analyzes how to integrate insights from the choices people make in the lab and the field into economic theory, applications, and empirical work. Enriches the standard economic model by improved understanding of people’s goals and tastes, as well as incorporating limits to rationality such as limited attention and memory, errors in statistical reasoning and social inference, shortcomings in self-regulation, and misprediction of utility. The course is intended for doctoral students interested in research in economics and related fields; we also strongly encourage undergraduates with appropriate preparation. Course ID: 119960

ECON 980BB: Behavioral Economics (FAS, Economics, Spring) Instructor(s): Tomasz Strzalecki. The seminar focuses on theoretical and experimental issues in behavioral economics. It focuses on two topics: risk and learning. How much risk are people willing to take and how their beliefs change in response to new data, such as the certainty effect, overconfidence, confirmation bias, belief polarization, stereotyping, etc. The course studies relationships between mathematical models of individual behavior and the kinds of behavior we can observe in the lab. They design experiments to test various theories and also study the types of behavior for which we don’t have good models yet, and try to understand what a good model would look like. Prerequisites/Notes: This is a junior tutorial. Course ID: 24433

MLD 304: Science of Behavior Change (HKS, Fall) Instructor(s): Todd Rogers. This course aims to improve students’ abilities to design policies and interventions that improve societal well-being.  It accomplishes this by focusing on how to leverage insights about human decision making to develop interventions (“nudges”).  They add to the toolbox that standard economics provides for influencing behavior (namely, incentives and information) with the insights from behavioral science. There are three additional, though secondary, goals for this class.  First, it will help you better understand the science of how humans make judgments and decisions.  We will review research on human thinking from social psychology, cognitive psychology, political science, organizational behavior, decision science, and economics (including its subfield, behavioral economics). Second, this course aims to improve the quality of your own judgments and decisions.  People are poor intuitive statisticians, meaning that when they “just think” about situations for which some data or casual observations exist, they tend to make serious inferential errors, in turn leading to systematically biased decisions. We will study some errors that are particularly important for real-world problems and look for easy‐to‐implement solutions. Third, this course aims to increase your familiarity with randomized experiments so you can be a smarter consumer of claims that interventions cause certain outcomes.  The class will be suffused with randomized experiments, and they repeatedly discuss how confident one can be that intervention X causes outcome Y.  Applications of the material covered in this course include policy design, healthcare, diversity and inclusion, energy, politics, education, finance, negotiation, risk management, human resource management, and organization of teams, among others. Course ID: 170483

API 303: Game Theory and Strategic Decisions (HKS, Spring) Instructor(s): Pinar Doğan. This course uses game theory to study strategic behavior in real-world situations. It develops theoretical concepts, such as incentives, strategies, threats and promises, and signaling, with application to a range of policy issues. Examples will be drawn from a wide variety of areas, such as competition, bargaining, auction design, and voting behavior. This course will also explore how people actually behave in strategic settings through a series of participatory demonstrations. Course ID: 170054

ECON 1050: Strategy, Conflict, and Cooperation (FAS, Economics, Spring) Instructor(s): Robert Neugeboren. Game theory is the study of interdependent decision-making. In the early days of the cold war, game theory was used to analyze an emerging nuclear arms race; today, it has applications in economics, psychology, politics, the law and other fields. In this course, we will explore the “strategic way of thinking” as developed by game theorists over the past sixty years. Special attention will be paid to the move from zero-sum to nonzero-sum game theory. Students will learn the basic solution concepts of game theory — including minimax and Nash equilibrium — by playing and analyzing games in class, and then we will take up some game-theoretic applications in negotiation settings: the strategic use of threats, bluffs and promises. We will also study the repeated prisoner’s dilemma and investigate how cooperative behavior may emerge in a population of rational egoists. This problem — “the evolution of cooperation” — extends from economics and political science to biology and artificial intelligence, and it presents a host of interesting challenges for both theoretical and applied research. Finally, we will consider the changing context for the development of game theory today, in particular, the need to achieve international cooperation on economic and environmental issues. The course has two main objectives: to introduce students to the fundamental problems and solution concepts of noncooperative game theory; and to provide an historical perspective on its development, from the analysis of military conflicts to contemporary applications in economics and other fields. No special mathematical preparation is required. Prerequisites/Notes: No special mathematical preparation is required. Course ID: 123893

ECON 2052: Game Theory I: Equilibrium Theory (FAS, Economics, Fall) Instructor(s): Shengwu Li. This is a course about game theory and mechanism design. The first half covers key concepts and techniques, and the second half surveys advanced topics near the research frontier. This course is taught assuming familiarity with first-year PhD-level microeconomic theory, of the kind taught in ECON2010A and ECON2010B. Course ID: 113349

HPM 252: Negotiation (HSPH, Spring) Instructor(s): Linda Kaboolian. The ability to negotiate successfully rests on a combination of analytic and interpersonal skills. Negotiators must execute promising strategies based on their analysis of the multitude of factors that affect the negotiation and that structure the definition of a successful outcome. Among these issues are the context and the structure of the negotiation, the interests of the other parties, the opportunities and barriers to creating and claiming value on a sustainable basis, and the range of possible moves and countermoves both at and away from the bargaining table, the value of the relationships, personal goals and ethical considerations. Interpersonal skills are important because negotiations are interactions with counterparts. Effective negotiators influence the behavior of other parties, correctly read the actions, intentions and preferences of counterparts, communicate their own perspectives and intentions well, and are aware of and can correct for their own cognitive and emotional biases. Strong interpersonal skills make it possible to execute one’s own strategy and react to moves by counterparts effectively. This course presents conceptual frameworks that will help students analyze negotiations in general and prepare more comprehensively for future negotiations in which they may be involved. In-class analysis of case studies and readings from applied game theory, social psychology, political theory, and behavioral economics, students will draw out lessons from ongoing, real-world negotiations. Through participation in negotiation simulations, they will have the opportunity to exercise their powers of communication and persuasion, and to experiment with a variety of negotiating strategies and tactics. The simulation exercises draw from a wide variety of contexts, and the aim is to illustrate concepts and tools that apply to a variety of negotiation settings. In-class debriefs of their experience as well as your outcomes will help them make adjustments to your negotiating practice that better reflect their intentions and preferences. In addition to developing a better understanding of strategy, students will learn a great deal about themselves in this course. They will have repeated exposure to situations that involve a shifting mix of opportunities for cooperation and competition as well as important ethical choices. The main pedagogical perspective is to improve their own repertoire of action practice by reflecting on your practice. As a result, their negotiating effectiveness should increase significantly. Overall, the instructor expects that students will finish the course as an analytically savvy, flexible, efficacious negotiator. Prerequisites/Notes: Students are required to attend class from the beginning of the term. The course begins with an in-class exercise. Throughout the semester, exceptions are made for professional responsibilities, illness, and family matters. Course ID: 190570

API 201: Quantitative Analysis and Empirical Methods (HKS, Fall). Instructor(s): Elise Swanson, Jonathan Borck, Nandita Krishnaswamy, Theodore Svoronos. This course introduces students to concepts and techniques essential to the empirical analysis of public policy issues. Provides an introduction to probability, statistics, and decision analysis, emphasizing the ways in which these tools are applied to practical policy questions. Topics include: applied probability; decision making under uncertainty; working with data; statistical inference; and hypothesis testing. The course also provides students an opportunity to become proficient in the use of generative AI tools to effectively analyze quantitative data. Course ID: 170029

API 222: Machine Learning and Big Data Analytics (HKS, Fall) Instructor(s): Soroush Saghafian. In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems. We will cover various recent techniques and their applications from supervised, unsupervised, and reinforcement learning. In addition, students will get the chance to work with some data sets using software and apply their knowledge to a variety of examples from a broad array of industries and policy domains. Some of the intended course topics (time permitting) include: K-Nearest Neighbors, Naive Bayes, Logistic Regression, Linear and Quadratic Discriminant Analysis, Model Selection (Cross Validation, Bootstrapping), Support Vector Machines, Smoothing Splines, Generalized Additive Models, Shrinkage Methods (Lasso, Ridge), Dimension Reduction Methods (Principal Component Regression, Partial Least Squares), Decision Trees, Bagging, Boosting, Random Forest, K-Means Clustering, Hierarchical Clustering, Neural Networks, Deep Learning, and Reinforcement Learning. Course ID: 208037

BST 210: Applied Regression Analysis (HSPH, Biostats, Fall and Spring) Instructor(s): Jonathan Larson, Tanayott Thaweethai. This course focuses on model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature. Course ID: 190025

BST 223: Applied Survival Analysis (HSPH, Biostats, Spring) Instructor(s): Harrison Reeder. This course focuses on survival analysis, or more generally time-to-event analysis, with the primary audience being graduate students pursuing a Master’s degree in biostatistics or a PhD in one of the other departments at the Harvard Chan School. Covered in the course will be: an introduction to various types of censoring and truncation that commonly arise; the mathematical representations of time-to-event distributions, such as via the hazard and survivor functions; nonparametric methods such as Kaplan-Meier estimation of the survivor function and log-rank test for hypothesis testing; semi-parametric and parametric regression modeling techniques, such as the Cox model, the accelerated failure time model, the additive hazards model and cure fraction models; survival analysis within the causal inference paradigm; the analysis of competing and semi-competing risks; outcome-dependent sampling schemes, such as nested case-control and case-cohort designs; and, power/sample size calculations for studies with time-to-event endpoints. Throughout, equal emphasis will be given to the theoretical/technical underpinnings of survival analysis and to the use of real world data examples. Coding examples and course support will focus on the statistical software R. Prerequisites/Notes: Prerequisites include BST210 or BST213 or BST 220 or BST 232 or BST 260 or PHS2000A. Lab or section times to be announced at first meeting. Course ID: 190040

EDU S052: Applied Data Analysis (GSE, Spring) Instructor(s): Andrew Ho, Melanie Rucinski. This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis and practice communicating their findings clearly to audiences of researchers, practitioners, and policymakers. S-052 contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess to perform sensible analyses of complex educational, psychological, and social data. The course begins by reviewing multivariate linear regression and continues with program evaluation, multilevel modeling, measurement, multivariate methods, and generalized linear models. Specific techniques covered include regression discontinuity, difference-in-differences, fixed and random effects modeling, reliability estimation, and principal components analysis. S-052 is an applied course. It offers conceptual explanations of statistical techniques and provides many opportunities to implement and interpret statistical analysis, including through statistical coding in either R or Stata. Prerequisites/Notes: Successful completion of S-040 or an equivalent course covering applied regression analysis through multivariate regression and interaction terms. Course ID: 180866

STAT 110: Introduction to Probability (FAS, Statistics, Fall) Instructor(s): Joseph Blitzstein. This course is a comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence. Course ID: 110766

BIOSTAT 249: Bayesian Methodology in Biostatistics (HSPH, Spring) Instructor(s): TBA. This course focuses on general principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trails, survival analysis. Course Note: Lab or section times to be announced at first meeting; cross-listed: Harvard Chan Students must register for the Harvard Chan course. Course ID: 190064

EPI 289: Epidemiologic Methods III: Models for Causal Inference (HSPH, Spring) Instructor(s): Barbra Dickerman. Causal Inference is a fundamental component of epidemiologic research. EPI289 describes models for causal inference, their application to epidemiologic data, and the assumptions required to endow the parameter estimates with a causal interpretation. The course introduces outcome regression, propensity score methods, the parametric g-formula, inverse probability weighting of marginal structural models, g-estimation of nested structural models, and instrumental variable methods. Each week students are asked to analyze the same data using a different method. Prerequisites/Notes: EPI289 is designed to be taken after EPI201/EPI202 and before EPI204 and EPI207. Epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289. This is the first course in the sequence of EPI core courses on modeling (EPI289, EPI204, EPI207). EPI289 focuses on time-fixed dichotomous treatments and time-fixed dichotomous and continuous outcomes. The course introduces failure time outcomes (survival analysis), which are extensively covered in EPI204, and time-varying treatments, which are extensively covered in EPI207. Familiarity with R is required. Course prerequisites are [(EPI 201 or EPI 208 or EPI 500) and (EPI 202 or EPI 202s)]. Course ID: 190332

STAT 220: Bayesian Data Analysis (FAS, Statistics, Spring) Instructor(s): Jun Liu. This course focuses on Bayesian theory, methods, and applications. Bayesian regression models, exponential family models, mixture models, and hierarchical models; decision analysis and frequentist properties; model checking and model comparison. Course ID: 118016

API 111: Microeconomic Theory I (HKS, Fall) Instructor(s): June Ma. This is a comprehensive course in economic theory designed for doctoral students in all parts of the university. Topics include consumption, production, choice under risk and uncertainty, markets, and general equilibrium theory. Topics will be motivated by appealing to related recent theoretical and applied economics research. Undergraduates with appropriate background are welcome, subject to the instructor’s approval. Course ID: 170008

ECON 1011A: Intermediate Microeconomic: Advanced (FAS, Economics, Fall) Instructor(s): Edward Glaeser. This course is similar to Economics 1010a, but more mathematical and covers more material. The course teaches the basic tools of economics and to apply them to a wide range of human behavior. Prerequisites/Notes: Prerequisites for this course include Mathematics 21a or permission of the instructor. Course ID: 120711

ECON 2120: Principles of Econometrics (FAS, Economics, Fall) Instructor(s): Elie Tamer. Linear predictor as approximation to conditional expectation function. Least-squares projection as sample counterpart. Splines. Omitted variable bias and panel data. Bayesian inference for parameters defined by moment conditions. Finite sample frequentist inference for the normal linear model. Statistical decision theory and dominating least squares with many predictor variables; applications to estimating fixed effects (teacher effects, place effects) using panel data. Asymptotic inference in the generalized method of moments framework. Likelihood inference using information measures to define best approximations within parametric models. Instrumental variable models and the role of random assignment; applications include models of demand and supply and the evaluation of treatment effects. Course ID: 115026

GHP 525: Econometrics for Health Policy (HSPH, Fall) Instructor(s): David Canning. This is a course in applied econometrics for doctoral and advanced master level students. The course has two primary objectives: (1) to develop skills in linking economic behavioural models and quantitative analysis, in a way that students can use in their own research; (2) to develop students’ abilities to understand and evaluate critically other peoples’ econometric studies. The course focuses on developing the theoretical basis and practical application of the most common empirical models used in modern health policy research. In particular, it pays special attention to a class of models identifying causal effects in observational data, including randomized trials, instrumental variable estimation, interrupted time series, difference-in-difference, regression discontinuity, and propensity score methods. The course will also discuss approaches to dealing with missing data. We will also examine machine learning models for out of sample prediction, including random forest and gradient boosting methods. Lectures will be complemented with computer exercises building on public domain data sets commonly used in health research, such as the Demographic and Health Surveys. Students will be expected to carry out a group project, using the methods taught in the course for a hypothesis and dataset of their own choosing. The statistical packages recommended for the exercises is STATA, R, or Python. Course activities include an pptional review and computer lab sessions. Prerequisites/Notes:  Students are expected to be familiar with probability theory (density and distribution functions) as well as the concepts underlying basic ordinary least square (OLS) estimation. Prerequisites include BST210 or BST213; or equivalent course taken at Harvard Chan or HGSE with instructor permission. Course ID: 190440