Harvard University
Courses relevant to decision science are available across Harvard University.
Decision Theory
ECON 2059: Decision Theory (FAS, Economics) Instructor: Tomasz Strzalecki. This course focuses on classical models of choice in abstract settings, as well as uncertain and intertemporal environments. We will also study recent models that incorporate insights from psychology, such as temptation and self-control. Prerequisite/Level: Primarily for graduate students. Course ID: 121331
APMTH 231: Decision Theory (FAS, Applied Mathematics, Spring) Instructor: Demba Ba. This course focuses on mathematical analysis of decision making and covers topics such as Bayesian inference and risk, maximum likelihood and nonparametric methods, algorithmic methods for decision rules: perceptrons, neural nets, and back propagation, hidden Markov models, Blum-Welch, principal and independent components. Prerequisite/Level: Primarily for undergraduate students. Course ID: 203548
Decision Analysis and Economic Evaluation
RDS 282: Economic Evaluation of Health Policy & Program Management (HSPH, Spring) Instructors: Stephen Resch and Lisa Robinson. This course uses a case-study approach to introduce students to 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); (2) controversies and limitations of CEA in practice; (3) the design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs; (4) the integration of evidence of economic value into 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. Prerequisite/Level: RDS280 or RDS286 (concurrent enrollment allowed). Prior coursework in microeconomics is recommended. Primarily for graduate students. Course ID: 191104
RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructors: Jane Kim and Nicolas Menzies. This is an intermediate-level course on the 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/Level: Familiarity with matrix algebra and elementary calculus may be helpful but not required; BST201 or ID201 and RDS280 or RDS286 (concurrent enrollment allowed). Primary for graduate students. Course ID: 191106
GHP 228: Quantitative Methods in Impact Evaluation (HSPH, Spring) Instructor: 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. 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. Prerequisite/Level: Econometrics and intermediate micro-economics are required for this course. The course is intended for doctoral students who are finishing their course work and aims to help them transition into independent research. Course ID: 190392
API 302: Analytic Frameworks for Policy (HKS, Fall) Instructor: Christopher Avery, 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. Prerequisite/Level: Primarily for graduate students. Course ID: 170053
FRSEMR 70E: Climate Change Economics: Analysis and Decisions (FAS, Spring) Instructor: Martin Weitzman. Climate change is one of the most difficult problems facing humanity. A small sample of questions to be asked and answers attempted in this seminar includes the following. How do we analyze and decide what to do about climate change? What are the basic models combining economics with climate science, what are these models telling us, and how do we choose among their varying messages? How are risk and uncertainty incorporated? How do we estimate future costs of carbon-light technologies? How do we quantify damages, including ecosystem damages? Who pays for what? Why are discounting and the choice of discount rate so critical to the analysis and what discount rate should we use? What is the social cost of carbon and how is it used? Which instruments (prices, quantities, standards, etc.) are available to control greenhouse gas emissions and what are the strengths and weaknesses of each? What is climate sensitivity and why is it, and the feedbacks it incorporates, so important? How should the possibility of catastrophic climate change be evaluated and incorporated? What are costs and benefits of geoengineering the planet to counter global warming? Why has climate change been characterized as the biggest international market failure of all time and how might the world resolve the associated free-rider problem? Prerequisite/Level: For undergraduate students. Course ID: 203008
Normative Frameworks for Social Choice
ID 250: Ethical Basis of the Practice of Public Health (HSPH, Fall) Instructors: Nir Eyal and 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. Prerequisite/Level: For graduate students. Course ID: 190768
GHP 230: Introduction to Economics with Applications to Health and Development (HSPH, Fall) Instructor: Margaret McConnell. This course provides an overview of the microeconomic theories and concepts most relevant for understanding health and development. 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. Prerequisite/Level: For graduate students. Course ID: 190394
Decision Analysis and Modeling
RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor: Ankur Pandya. This course introduces students to the methods and applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives are: (1) to provide a basic technical understanding of the methods used in decision analysis, (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 developed and developing countries. Prerequisite/Level: Introductory economics is recommended but not required. Prerequisites/Level: BST200 or BST201 or BST202 and BST203 or BST206 and BST207 or BST206 and BST208 or BST206 and BST209 (concurrent enrollment allowed). Primarily for graduate students. Course ID: 191102
EPI 260: Mathematical Modeling of Infectious Diseases (HSPH, Spring) Instructor: Marc Lipsitch. This course will cover 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 will 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 will 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. Prerequisite/Level: EPI501, may be taken concurrently, previous course in calculus required. Primarily for graduate students. Course ID: 190321
EPI 501: Dynamics of Infectious Diseases (HSPH, Spring) Instructor: Caroline Buckee. 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. Prerequisite/Level: For graduate students. Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 190352
APMTH 115/ ENG-SCI 115: Mathematical Modeling (SEAS, Fall/FAS, Spring) Instructors: Zhiming Kuang, Lakshiminarayanan Mahadevan. 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. Prerequisite/Level: Must take APMTH105 or APMTH108 or APMTH104 or MATH112 before taking APMTH115. For both graduate and undergraduate students. Course ID: 156427
APMTH 222: Stochastic Modeling (FAS, Applied Math, Spring) Instructors: Nikolaos Trichakis and Joel Goh. The course covers the modeling, analysis, and control of stochastic systems. Topics include Bernoulli and Poisson processes, Markov chains and Markov decision processes, optimization under uncertainty, queuing theory, and simulation. Applications will be presented in healthcare, inventory management, and service systems. Prerequisite/Level: Recommended: Applied Mathematics 121. For both graduate and undergraduate students. Course ID: 109344
MGMT S-5070: Decision Support Models and Spreadsheet Analysis (Harvard Summer School) Instructor: Philip Anthony Vaccaro. This course introduces nonmathematical managers to the major quantitative models designed for effective decision making in today’s complex and increasingly uncertain operating environment. The course is relevant to manufacturing, service, institutional, and government sectors as well as marketing, finance, and management. Topics include goal programming, simulation, decision trees, probability, optimization, and scenario analysis. Emphasis is placed on a general understanding of theory, mechanics, application potential, and user-friendly software packages. Course ID: 33848
E-PSCI 236: Environmental Modeling and Data Analysis (FAS, Earth & Planetary Sciences, Fall) Instructor: Steven Wofsy and Daniel Jacob. This course covers topics that include chemical transport models (principles and numerical methods), inverse models (Bayes’ theorem, optimal estimation, Kalman filter, adjoint methods), and analysis of environmental data (visualization, time series analysis, Monte Carlo methods, statistical assessment). Students prepare projects and presentations. Prerequisite/Level: Primarily for graduate students. Course ID: 120783
Optimization/Management Science/Operations Research
MLD 601: Operations Management (HKS, Fall) Instructor: 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. The course is oriented toward the general manager or those interested in an introduction to the field. Prerequisite/Level: Primarily for graduate students. Course ID: 170531
Engineering Systems Analysis for Design (MIT, Fall) Instructor: Richard De Neufville. This course covers theory and methods to identify, value, and implement flexibility in design, also known as “real options.” Topics include definition of uncertainties, simulation of performance for scenarios, screening models to identify desirable flexibility, decision and lattice analysis, and multidimensional economic evaluation. Students demonstrate proficiency through an extended application to a systems design of their choice. Prerequisite/Level: For graduate students. Course ID: 1.146
Optimization Methods (MIT, Fall) Instructors: D. Bertsimas and P. Parrilo. This course introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. It emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton’s method, heuristic methods, and dynamic programming and optimal control methods. Prerequisite/Level: Primarily for undergraduate students Expectations and evaluation criteria differ for students taking graduate version. Course ID: 6.255
Optimization Methods in Business Analytics (MIT, Spring) Instructor: James B. Orlin. This course Introduces optimization methods with a focus on modeling, solution techniques, and analysis. Covers linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, data analysis, transportation, marketing, project management, and finance. Includes a project in which student teams select and solve an optimization problem (possibly a large-scale problem) of practical interest. Prerequisite/Level: Primarily for undergraduate students. Course ID: 15.053
MLD 603M: Operations Management for Improving Public Services (HKS, Spring 2019) Instructor: Soroush Saghafian. How can organizations design and implement effective policies to improve their services with limited resources? The course provides students with quantitative and qualitative tools from management, economics, mathematics, finance, business, computer science, and other fields that can enable public, nonprofit, and private sector organizations to effectively improve their performance. The course draws upon cutting edge research and examples from a broad array of industries including healthcare, business services, government, retail, and transportation, among others. Prerequisite/Level: Primarily for graduate students.
COMPSCI 234R: Topics on Computation in Networks and Crowds (SEAS, Fall and Spring) Instructor: Nicole Immorlica. This course covers topics on the design and analysis of algorithms, processes, and systems related to crowds and social networks. Readings in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research. Prerequisite/Level: Mathematics 21b, Applied Mathematics 21b, or equivalent; Computer Science 124, and 181 or 182, or equivalents; or permission of instructor. Primarily for graduate students. Course ID: 109667
ENG-SCI 121/ MTH 121: Introduction to Optimization: Models and Methods (FAS, Fall) Instructor: Yiling Chen, David Parkes. Introduction to basic mathematical ideas and computational methods for solving deterministic and stochastic optimization problems. Topics covered: linear programming, integer programming, branch-and-bound, branch-and-cut, Markov chains, Markov decision processes. Emphasis on modeling. Examples from business, society, engineering, sports, e-commerce. Exercises in AMPL, complemented by Maple or Matlab. Prerequisite/Level: Linear algebra and some knowledge of probability and statistics at the level of Statistics 110 or Applied Mathematics 101 or permission of instructor. For both graduate and undergraduate students. Course ID: 156288
Behavioral Economics/Decision Psychology
ECON 2030: Psychology and Economics (FAS, Economics, Spring) Instructors: David Laibson and Andrei Shleifer. This course explores economic and psychological models of human behavior. Topics include bounded rationality, intertemporal choice, decision making under uncertainty, inference, choice heuristics, and social preferences. Economic applications include asset pricing, corporate finance, macroeconomics, labor, development, and industrial organization. Prerequisite/Level: Knowledge of multivariable calculus and econometrics necessary. Primarily for graduate students but open to undergraduates. Course ID: 119960
API 304: Behavioral Economics and Public Policy (HKS, Fall) Instructor: Brigitte Madrian. This course will examine the relationship between behavioral economics and public policy. Individuals frequently make decisions that systematically depart from the predictions of standard economic models. Behavioral economics attempts to understand these departures by integrating an understanding of the psychology of human behavior into economic analysis. The course will review the major themes of behavioral economics and address the implications for public policy in a wide variety of domains, including: retirement savings, social security, labor markets, household borrowing (credit cards, mortgages, payday lending), education, energy use, health care, addiction, organ donation, tax collection and compliance, and social welfare programs. Prerequisite/Level: Primarily for graduate students. Course ID: 203412
2582: Judgment and Decision-Making (HLS, Spring) Instructor: Bruce Hay. This course examines human judgment and decision making, with emphasis on the ways in which people depart from rational and/or ethical standards, particularly in groups and organizational settings. The course combines insights from multiple disciplines, including cognitive psychology, behavioral economics, and negotiation theory. Prerequisite/Level: Primarily for graduate students.
PSY 1584: Leadership Decision Making (FAS, Spring) Instructor: Jennifer S. Lerner. Course topics will include (a) fundamental mental processes in perception, memory and context dependence; (b) how questions affect answers; (c) models of decision making; (d) heuristics and biases; (e) social and group influences; (f) common traps; and (g) debiasing techniques. Throughout the course, the overarching goals are to: (1) Learn about the academic field of behavioral decision making, its major theories, results, and debates. (2) Become a critical consumer of research findings, learning methodological standards for evaluating the soundness of empirical studies. (3) Develop the ability to effectively write and speak about behavioral science theories, results, and debates. (4) Acquire practical skills for improving your own judgments and decisions. (5) Acquire knowledge of which biases individuals can fix with training/knowledge and which biases individuals cannot fix unless leaders engage in institutional design (e.g., nudges). (6) Develop a capstone project in which you apply the material in a way that will improve professional decision making processes. Prerequisite/Level: Primarily for graduate students. Doctoral students will have customized assignments and an additional meeting time in order to receive credit. Advanced undergraduates may enroll only by permission of the instructor. No prerequisites are required but introductory coursework in psychology and economics will be a significant help. Course ID: 205646
MLD 304: Science of Behavior Change (HKS, Spring) Instructor: Todd Rogers. This course is devoted to understanding the nature, causes, implications and applications of how people’s decisions deviate from optimal choices as well as the consequences of such deviations. This course focuses on how these judgment, decision-making and behavior tendencies can inform the design and development of welfare-enhancing interventions with the object to improve students’ abilities to design policies and interventions that improve societal well-being. Prerequisite/Level: Primarily for graduate students. Course ID: 170483
PSY 2650/HBS 4420: Behavioral Approaches to Decision Making and Negotiation (FAS/HBS, Fall) Instructor: Francesca Gino. Research overview of behavioral decision making and decision analytic perspectives to negotiation. Explores bounded rationality, decision biases, human decision making. Develops a behavioral decision perspective to negotiation, and examines how the field is currently evolving. Prerequisite/Level: Primarily for graduate students, but open to juniors and seniors in psychology and economics who are writing, or plan to write, a senior thesis. Course ID: 115060
PSY 2670a: Decision Making and the Psychology of Possibility (FAS, Fall) Instructor: Ellen Langer. Topics in decision making such as rationality, risk-taking, helplessness, and health are examined through the lens of mindfulness theory. Special emphasis given to the psychology of possibility in applied settings. Prerequisite/Level: Primarily for graduate students, but open to qualified undergraduates. Course ID: 131189
PSY 2670b: Decision Making and the Psychology of Possibility II (FAS, Spring) Instructor: Ellen Langer. A deeper exploration into the theoretical and experimental issues pertaining to decision making and the psychology of possibility, raised in Psychology 2670a. Prerequisite/Level: Psychology 2670a, primarily for graduate students, but open to qualified undergraduates. Course ID: 132599
Game Theory/Negotiation
ECON 1050: Strategy, Conflict, and Cooperation (FAS, Economics, Spring) Instructor: Robert Neugeboren. Game theory is the study of interdependent decision-making. In this course, students 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. Students We will also study the repeated prisoner’s dilemma and investigate how cooperative behavior may emerge in a population of rational egoists. The course has two main objective: 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. Prerequisite/Level: For graduate and undergraduate students. Course ID: 123893
ECON 2052: Game Theory I: Equilibrium Theory (FAS, Economics, Spring) Instructor: TBA. This course covers equilibrium analysis and its applications. Topics vary, but typically include equilibrium refinements (sequential equilibrium), the equilibria of various classes of games (repeated games, auctions, signaling games) and the definition and application of common knowledge. Prerequisite/Level: Economics 2010a or permission of the instructor. Primarily for graduate students. Course ID: 113349
HPM 252: Negotiation (HSPH, Spring) Instructor: Linda Kaboolian. This course will present conceptual frameworks that will help you analyze negotiations in general and prepare more comprehensively for future negotiations in which you may be involved. In class analysis of case studies and readings from applied game theory, social psychology, political theory and behavioral economics, we will draw out lessons from ongoing, real-world negotiations. Through participation in negotiation simulations, you will have the opportunity to exercise your 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 their aim is to illustrate concepts and tools that apply to a variety of negotiations settings. In-class debriefs of your experience as well as your outcomes will help you make adjustments in your negotiating practice that better reflect your intentions and preferences. Prerequisite/Level: Primarily for graduate students, with priority going to DrPH students. Course ID: 190570
MLD 222M: Negotiation Analysis (HKS, Fall) Instructor: Kessely Hong. This course introduces students to the theory and practice of negotiation by emphasizing both analytical and interpersonal skills. Analysis is important because negotiators cannot develop promising strategies without a deep understanding of the context of the situation, and the incentives, interests and alternatives of the other parties. Interpersonal skills are important because negotiation is essentially a process of communication, trust building (or breaking), and mutual persuasion. Through case discussions, students will develop a set of conceptual frameworks to help students diagnose barriers to agreement and develop creative strategies to address them. Through participation in negotiation simulations, students will have the opportunity to learn how to prepare effectively, to practice communication and persuasion, and to experiment with a variety of negotiation tactics and strategies to both create and claim value. The goal of the course is to help students be better equipped to anticipate challenges in advance, to expand their conception of “what is possible” in order to develop creative and wise strategies, to build sustainable coalitions to support their goals, and to gain confidence in advocating for themselves and others. Prerequisite/Level: For graduate students. Course ID: 170474
MLD 224B: Behavioral Science of Negotiation (HKS, Fall) Instructor: Julia Minson. This course is designed to build your understanding, skill, and confidence so that you achieve better outcomes in all your negotiations — large and small. In this course you will learn how to increase the quality of the agreements you negotiate so as to maximize potential value, and also how to claim as much of that value for yourself as you can. You will learn to see opportunities to negotiate where you had never seen them before. A basic premise of the course is that great negotiators are not born, but made through thoughtful, evidence-based skill building. Thus, the course is structured around three types of activities: 1) Applying analytical skills to gain a strategic understanding of negotiation contexts; 2) Learning empirically validated techniques for advancing your interests; 3) Practice, practice, and more practice. Prerequisite/Level: For graduate students. Course ID: 170476
PSY 1500: Decision Making and Negotiation (FAS, Psychology, Spring) Instructor: Christine Looser. People make decisions and negotiate with others from a young age; however, most know little about the strategy and the psychology that lie beneath these sophisticated behaviors. This class is designed to look under the hood at the cognitive processes at play when we make decisions and negotiate with others. We will discuss original research articles and use simulated class exercises to gain a better understanding of how the human mind makes choices and persuades others. Prerequisite/Level: Science of Living Systems 20 (or equivalent) and PSY 15. For undergraduate students. Course ID: 160692
Statistical Methods
API 201: Quantitative Analysis and Empirical Methods (HKS, Fall). Instructors: Jonathan Borck, Dan Levy, Theodore Syoronos, and Soroush Saghafian. This course introduces students to concepts and techniques essential to the analysis of public policy issues. It provides an introduction to probability, statistics, and decision analysis emphasizing the ways in which these tools are applied to practical policy questions. Topics include: descriptive statistics; basic probability; conditional probability; Bayes’ rule; decision making under uncertainty; statistical inference; hypothesis testing; and bivariate regression analysis. The course also provides students an opportunity to become proficient in the use of computer software widely used in analyzing quantitative data. Prerequisite/Level: Graduate MPP students only. Course ID: 170029
BST 210: Applied Regression Analysis (HSPH, Biostats, Spring) Instructor: Robert Glynn, David Wypij. This course emphasizes concepts and methods for analysis of data which are categorical, rate-of-occurrence (e.g., incidence rate), and time-to-event (survival duration). It stresses applications in epidemiology, clinical trials, and other public health research. Topics include measures of association, 2×2 tables, stratification, matched pairs, logistic regression, model building, analysis of rates, and survival data analysis using proportional hazards models. Prerequisite/Level: BIO200 or BIO201 or ID200 or ID207 or BIO202 and BIO203 or BIO206 and BIO207 or BIO206 and BIO208 or BIO206 and BIO209. Primarily for graduate students. Course ID: 190025
BST 223: Applied Survival Analysis (HSPH, Biostats, Spring) Instructor: Andrea Bellavia. This course covers types of censoring, hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and the accelerated failure time model, adjustment for time- varying covariates, and the use of parametric distributions (exponential, Weibull) in survival analysis. Methods for recurrent survival outcomes and competing risks will also be discussed, as well as design of studies with survival outcomes. Class material will include presentation of statistical methods for estimation and testing along with current software (SAS, Stata) for implementing analyses of survival data. Applications to real data will be emphasized. Prerequisite/Level: BIO210 or BIO213 or BIO230. Primarily for graduate students. Course ID: 190040
EDU S052: Applied Data Analysis (GSE, Spring) Instructor: Andrew Ho. This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis, and who want to communicate their findings clearly to audiences of researchers, scholars, and policymakers. The course contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform sensible analyses of complex educational, psychological, and social data. Topics in the course include more extensive use of transformations in regression analysis, influence statistics, building and comparing taxonomies of regression models, general linear hypothesis testing, an introduction to multilevel modeling, nonlinear regression analysis, binomial logistic regression analysis, principal components analysis, cluster analysis, introduction to discrete-time survival analysis, and others. S-052 is an applied course that offers conceptual explanations of statistical techniques, along with opportunities to examine, implement, and practice them in real data. Prerequisite/Level: Primarily for graduate students. Course ID: 180866
Probability Theory/Bayes
STAT 220: Bayesian Data Analysis (FAS, Statistics, Spring) Instructor: Jun Liu. This course covers basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions, and includes methods for monitoring adequacy of models and examining sensitivity of models. Prerequisite/Level: Recommended Statistics 110 and 111. Primarily for graduate students. Course ID: 118016
BST 249: Bayesian Methodology in Biostatistics (HSPH, Fall) Instructor: Lorenzo Trippa. This course introduces general principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison as well as Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials and survival analysis. Prerequisite/Level: Biostatistics 231 and Biostatistics 232, or signature of instructor required. Primarily for graduate students. Course ID: 190064
EPI 289: Models for Causal Inference (HSPH, Spring) Instructor: Miguel Hernan. This course describes models for causal inference, their application to epidemiologic data, and the assumptions required to endow the parameter estimates with a causal interpretation. It 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. EPI289 is the first course in the sequence of EPI core courses on modeling (EPI289, EPI204, EPI207). Familiarity with the SAS language is strongly recommended for all courses in the sequence. Prerequisite/Level: EPI201 and EPI202 Infectious Disease Modeling. Primarily for graduate students. Course ID: 190332
Economics
ECON 2120: Principles of Econometrics (FAS, Economics, Fall) Instructor: Gary Chamberlain. This course provides an introduction to methods employed in applied econometrics, including linear regression, instrumental variables, panel data techniques, generalized method of moments, and maximum likelihood. Prerequisite/Level: Linear algebra, probability at level of Statistics 110. Enrollment is limited to PhD candidates in economics, business economics, health policy, public policy, and political economy and government (PEG). Qualified undergraduates can also enroll. Course ID: 115026
GHP 525: Econometrics for Health Policy (HSPH, Fall) Instructor: Victoria Fan. This course has two primary objectives: (1) to develop skills in linking economic behavioral 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 health policy research. In particular, it pays special attention to a class of models identifying causal effects in observational data, including instrumental variable estimation, simultaneous equations and two-stage- least-squares, quasi-experiments and difference-in-difference method, sample selection, treatment effect models and propensity score methods. Prerequisite/Level: 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, and have taken BIO210 or BIO211 or BIO213. Primarily for doctoral and advanced master level students. Course ID: 190440
API 111: Microeconomic Theory I (HKS, Fall) Instructor: Maciej Kotowski. This course provides a comprehensive overview of economic theory. Topics include consumption, production, behavior toward risk, markets, and general equilibrium theory. Also looks at applications to policy analysis, business decisions, industrial organization, finance, and the legal system. Prerequisite/Level: Multivariate calculus and one course in probability theory. Thorough background in microeconomic theory at the intermediate level. Primarily for graduate students, but undergraduates with appropriate background, with instructor permission. Course ID: 112942