Computing in Context
This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.
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Instructor:
Aidan Feldman
Info - 3 points
Computing in Context
This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.
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Instructor:
Aidan Feldman
Info - 3 points
Applied Econometrics
This course equips students with the tools to critically evaluate empirical research through the lens of causal inference. Emphasizing real-world policy relevance over statistical correlation, it introduces students to identification strategies that approximate randomized trials using observational data. Students will explore advanced econometric methods, including instrumental variables, difference-in-differences, fixed effects, regression discontinuity, and synthetic controls, while examining their strengths and limitations in drawing causal conclusions.
Designed for students with prior coursework in quantitative methods (U6500 and U6501), this course stresses conceptual rigor and applied skills. Assignments include STATA-based replication exercises, a research design proposal, and seminar engagement. Readings and examples draw from policy-relevant domains such as health, education, and environmental economics. Students will leave the course with a deeper understanding of how to produce, assess, and apply causal evidence to inform public decision-making.
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Instructor:
Douglas V Almond
Info - 3 points
Data Analysis for Policy Research Using
Data for Policy Research
This course develops the skills necessary to prepare, analyze, and present data for policy analysis and program evaluation using R. Building on the foundations from Quant I and II—probability, statistics, regression analysis, and causal inference—this course emphasizes the practical application of microeconometric methods to real-world policy questions. (Note: macroeconomic topics and forecasting methods are not covered.)
The central objective is to train students to be effective analysts and policy researchers. Key questions include: Given the available data, what analysis best informs the policy question? How should we design research, prepare data, and implement statistical methods using R? How can we assess causal effects of policies rather than mere correlations? What ethical considerations arise when working with data on marginalized populations?
Students will learn through hands-on analysis of datasets tied to a range of policy issues, including: caste-based expenditure gaps in India, racial disparities in NYPD fare evasion enforcement, water shutoffs in Detroit, Village Fund grants in Indonesia, public health insurance and child mortality, and Stand Your Ground laws and gun violence. The course culminates in a student-led project on a policy topic of their choosing.
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Instructor:
Harold B Stolper
Info - 3 points
Data Analysis for Policy Research Using
Data for Policy Research
This course develops the skills necessary to prepare, analyze, and present data for policy analysis and program evaluation using R. Building on the foundations from Quant I and II—probability, statistics, regression analysis, and causal inference—this course emphasizes the practical application of microeconometric methods to real-world policy questions. (Note: macroeconomic topics and forecasting methods are not covered.)
The central objective is to train students to be effective analysts and policy researchers. Key questions include: Given the available data, what analysis best informs the policy question? How should we design research, prepare data, and implement statistical methods using R? How can we assess causal effects of policies rather than mere correlations? What ethical considerations arise when working with data on marginalized populations?
Students will learn through hands-on analysis of datasets tied to a range of policy issues, including: caste-based expenditure gaps in India, racial disparities in NYPD fare evasion enforcement, water shutoffs in Detroit, Village Fund grants in Indonesia, public health insurance and child mortality, and Stand Your Ground laws and gun violence. The course culminates in a student-led project on a policy topic of their choosing.
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Instructor:
Harold B Stolper
Info - 3 points