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

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

EH 550: Special Topics in Environmental Health: Data Science Methods for Causal Attribution in Climate-Health Instructor(s): John Evans, Lisa Robinson. The course is designed for MS and PhD students in Environmental Health and other departments who intend to develop, apply or interpret risk assessments in support of environmental decision making and policy. It asks each student to consider how they would decide which environmental exposures pose risks so large that they must be controlled, and which are so small that they may safely be ignored. For those which must be controlled, it asks them to develop an approach for deciding how much control is necessary and how to distinguish circumstances where they must act now from those where uncertainty is so large that control decisions should be deferred (in order to allow researchers time to conduct studies intended to improve understanding of the risks involved). The distinct roles of science and values are discussed. We consider the complexity introduced when individual values (health vs. wealth, risk neutrality-aversion-proneness) differ substantially across the affected population. The course introduces the concepts and framework of economic evaluation; reviews approaches for valuing risks to health and longevity; discusses methods for incorporating consideration of the distribution and equity of control costs and health benefits. It examines the interpretation of evidence from epidemiology and toxicology in risk assessment. It considers the sources of uncertainty inherent in estimates of reference doses and cancer potencies derived from animal studies, in vitro analyses and structure-activity relationships, and effect estimates from epidemiological studies and introduces approaches for characterization and analysis of the propagation of uncertainty. With this knowledge in hand, we explore the impact of uncertainty on decisions, both about emissions control and about research needs. We seek a set of principles, solidly grounded in theory, and a toolbox of approaches for applying these principles, which will support more informed decision making about environmental controls. Prerequisites/Notes: EH 206: Foundations of Environmental Health OR in previous years — RDS 500: Risk Assessment and EH 510: Exposure Assessment, or permission of instructor. Course ID: 207083

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

APMTH 215: Mathematical Modeling for Computational Science (FAS, Applied Mathematics, Fall) Instructor(s): Michael P. Brenner. Mathematical modeling is the essential component of the revolution in computation-based research over the past decade. While designing mathematical models is itself an art form, it is equally important to learn how to transform them into computational systems that allow robust evaluation, which is critical for real-world use cases, from modeling the spread of COVID, to designing better large language models.  This course introduces mathematical modeling ideas while teaching how to transform them into robust computational frameworks for model evaluation and deployment, as done in industry. The aim is to give both a broad view of “what a mathematical model” is and, at the same time, to teach the core computational skills for building usable state-of-the-art models. Topics drawn from biology, economics, engineering, physical and social sciences. This class is taught in parallel to the undergraduate class Applied Math 115. Preference will be given to Data Science and CSE SM Students and then other graduate students. A computer programming background is recommended, and to have taken the courses Statistics 110, Applied Mathematics 105. Course ID: 225020

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

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

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 2038: Cognitive Economics (FAS, Economics, Fall). Instructor(s): Andrei Shleifer, Joshua Schwartzstein. This course examines how psychological and cognitive processes influence economic decision-making. Topics include attention, memory, perception, categorization, belief formation, mental models, and the ways that individuals process information when making choices. The course is relevant to behavioral decision theory because it connects cognitive science with formal economic models of preferences, beliefs, and choice. Course ID: 226385

ECON 980BB: Behavioral Economics of Risk and Learning (FAS, Economics, Fall) Instructor(s): Tomasz Strzalecki. The seminar will focus on theoretical and experimental issues in behavioral economics. We will focus on two topics: risk and learning. How much risk are people willing to take and how their beliefs change in response to new data, so things like certainty effect, overconfidence, confirmation bias, belief polarization, stereotyping, etc. We will study relationships between mathematical models of individual behavior and the kinds of behavior we can observe in the lab. We will 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. This is a junior tutorial. Prerequisites/Notes: Prior knowledge of behavioral economics will be useful but not necessary. The course will focus on analytical methods and therefore requires knowledge of probability and statistics. Course ID: 156369

MLD 308: Leadership Decision Making: From Individuals to Institutions (HKS, Government, Spring) Instructor(s): Jennifer Lerner.  From public health to global security, leaders face high-stakes decisions under deep uncertainty. Navigating these challenges requires understanding how human judgment operates—where it is deeply valuable and where it is systematically prone to error. As machine intelligence takes on a growing role in routine analysis and prediction, the quality of human judgment becomes increasingly consequential. Drawing on research from psychological science, behavioral economics, neuroscience, and ethics, this course provides a scientific foundation for improving decisions by revealing how people perceive risk, interpret information, and make choices. By integrating theory with practice, students learn to anticipate behavioral responses and design policies and communications that achieve their intended effects. A central component of the course is the analysis and redesign of real-world choice architectures to improve clarity, accessibility, and decision quality while preserving individual agency. Students will leave the course with a structured approach to improving judgment and decision-making in organizations and public policy. There are no formal prerequisites. However, prior courses in psychology, behavioral economics (including MLD-304*), and/or statistics may be helpful. For HKS students, the course qualifies for the HKS Data and Research Methods Track as well as the Certificate in Management, Leadership, and Decision Science. Cross registrants are welcome. No auditors are permitted, unfortunately. * While some overlap between MLD-308 and MLD-304 is intentionally included, each course also covers a set of novel topics not covered in the other. Thus, students may take each one before the other. Course ID: 170486

PSY 1322: Decisions Big and Small: The Cognitive Science of Making Up Your Mind (FAS, Psychology, Spring) Instructor(s): Tomer Ullman. Life is full of decisions, but not all decisions are made equal. Choices can be big and consequential (should I focus on my success, family, or passion), or small and everyday (going out, or staying in). This course will introduce you to the cognitive science of judging and choosing. You will learn about 1) Rational planning, the kind a perfect intelligence might carry out 2) Common simplifications and shortcuts that non-perfect humans use, and how these may actually be appealing approximations for any decision-making system 3) Regret over choices taken and not taken 4) Making decisions with others 5) Transformative decisions, the ones that change who you are as a person. As we cover these topics, we will consider how to apply the insights from the psychology of decision making to your own ordinary and extraordinary choices. The Psychology Department requires completion of Science of Living Systems 20 or Psychology 1 or the equivalent of introductory psychology (e.g. Psych AP=5 or IB =7 or Psyc S-1) and at least one foundational course from MCB/NEURO 80, PSY 11, PSY 14, PSY 15, PSY 16, or PSY 18 before enrolling in this course; or permission of instructor. Prerequisites/Notes: Prerequisites are PSY1 or Psychology AP=5 or Psychology IB=7 or Psyc S-1 AND PSY11 or PSY14 or PSY15 or PSY16 or PSY18 or NEURO80. Course ID: 212749

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

ECON 1057: Game Theory with Applications to Social Behavior (FAS, Economics, Fall) Instructor(s): Erez Yoeli. Game theory is the formal toolkit for analyzing situations in which payoffs depend not only on your actions (say, which TV series you watch), but also others’ (whether your friends are watching the same show).  You’ve probably already heard of some famous games, like the prisoners’ dilemma and the costly signaling game.  We’ll teach you to solve games like these, and more, using tools like Nash equilibrium, subgame perfection, Bayesian Nash equilibrium, and the one-shot deviation principle. Game theory has traditionally been applied to understand the behavior of highly deliberate agents, like heads of state, firms in an oligopoly, or participants in an auction.  However, we’ll apply game theory to social behavior typically considered the realm of psychologists and philosophers, such as why we speak indirectly, in what sense beauty is socially constructed, and where our moral intuitions come from. Each week, students are expected to complete a problem set, to read 2-3 academic papers, and to complete a 1-2 page response to short essay questions (‘prompts’) on these readings.  All assignments can be completed in groups of two. Tutorials are not required but are highly recommended for students without a substantial background, especially in math.  There will also be a final exam. Recommended prep includes Math 18a or Math 21a or Applied Math 21a, or talk to instructor. Course ID: 203555

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

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

STAT 221: Computational Statistics: Monte Carlo and Optimization (FAS, Statistics, Spring) Instructor(s): Alex Young. Computational tools for statistical inference and learning, with emphasis on the computational aspects of statistics, the statistical implications of inference algorithms, and the mathematical foundations for these ideas. Topics include: optimization methods such as Newton-Raphson and gradient-based algorithms; the EM algorithm; variational approximations; Monte Carlo methods, including Markov chain Monte Carlo, importance sampling, data augmentation, and sequential Monte Carlo; generative ML methods including diffusions and normalizing flows. Prerequisites/Notes: Computer programming exercises will apply the methods discussed in class.  Prerequisites include (STAT 110 or STAT 210) AND (STAT 171 or STAT 212) (STAT 212 may be taken concurrently). Course ID: 115077

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