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.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study.

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.

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.)

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. This course applies empirical economic tools to the study of education policy, with a focus on both K-12 and higher education systems. Topics include class size, peer effects, teacher quality, school accountability, school choice, vouchers, and student incentives.

Pre-requisites: A calculus-based micro-economics course (SIPA IA6400) or equivalent. This is an advanced course in development economics, designed for SIPA students interested in rigorous, applied training. Coursework includes extensive empirical exercises, requiring programming in Stata. The treatment of theoretical models presumes knowledge of calculus.

This course will cover practical time series forecasting techniques and consists of two parts. The first part focuses on the Box-Jenkins approach (ARIMA), including identification (selection) of the appropriate model, estimation of its parameters, and diagnostic checking of model adequacy. The second part of the course is on nonlinear models for time series, with emphasis on conditional volatility and ARCH models. By the end of the course, you will be able to apply these techniques to actual data, primarily financial and economic time series. 

Discrimination is the differential treatment of people based on identity or perceived identity (race, gender, ethnicity, LGBTQ+ status, age, religion, disability, immigration status, etc.). Such behavior violates some legal, social, and moral norms and has a negative impact on those discriminated against.

For these and other reasons, it is important to be able to formally identify discrimination from data. But how do we know that A’s treatment of B is because of B’s identity as opposed to some other characteristic of B or A that we may not even have a variable for?

Pre-req: any Quant III course. Instructor Managed Registration.

The course aims to analyze dynamic, multivariate interactions in evolutionary and non-stationary processes. The course first considers stationary univariate time-series processes and then extend the analysis to non-stationary processes and multivariate processes. The course covers a review of linear dynamic time-series models and focus on the concept of cointegration, as many applications lend themselves to dynamic systems of equilibrium-correction relations.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. Instructor permission required. Join the waitlist in Vergil to request registration. 

This course bridges the gap between data science and public policy by bringing together students from diverse academic backgrounds to address contemporary policy challenges using large-scale data. With the rapid growth of digital information and the increasing influence of machine learning and AI on public life, the ability to work across disciplines is becoming essential.

Pre-req: SIPA IA6500 - Quant I, and prior experience with R are required. Instructor permission required. Join the waitlist in Vergil to request registration. 

Pre-req: DSPC IA6000 - Computing in Context, or see option for testing out. In Computing in Context, students "explore[d] computing concepts and coding in the context of solving policy problems." Building off that foundation of Python fundamentals and data analysis, Advanced Computing for Policy goes both deeper and broader.

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.)

This course provides a rigorous introduction to renewable energy project finance modeling, focusing on the concepts, structures, and financial mechanisms that underpin investment in renewable energy projects such as wind and solar. Through lectures, demonstrations, and guided analysis of actual project documents and contracts, students will develop a comprehensive understanding of the key drivers of renewable energy economics and financing.

This course will be useful for students who are committed to evidence-based operations, programming, strategy, and overall effectiveness. Impact evaluations, combined with strong data systems, are integral tools for this evidence-driven work. At the end of the course, students will understand why and when to conduct impact evaluations, how to manage one, and how to recognize and differentiate a good impact evaluation from a non-rigorous one.

Spring 2026 Course Dates: March 27-28 & April 3-4

This course examines the principles and practices of monitoring and evaluation (M&E) in international development and humanitarian assistance. Students will learn to design theories of change, develop indicators, plan and conduct evaluations, and communicate results effectively. Emphasis is placed on adaptive management, complexity-aware approaches, and emerging trends such as equitable and decolonized evaluation and the integration of generative AI tools.

This course explores the intersection of financial risk management and public policy, focusing on the regulatory and institutional frameworks that have evolved since the global financial crisis. Students will learn to apply core risk management concepts—such as market, credit, counterparty, and liquidity risk—in assessing financial stability and regulatory policy. The course emphasizes intuition and applied techniques, using graphical and numerical methods rather than advanced math.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500). Priority Registration: MIA and MPA. Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools.

This course equips students with the skills and tools to design, assess, and manage impact measurement and evaluation (M&E) strategies within sustainable development and social impact contexts. Emphasizing both technical rigor and real-world application, the course prepares students to develop M&E frameworks, apply theories of change, track and evaluate outcomes, and communicate findings to diverse audiences.

This course provides an applied introduction to cost-benefit analysis (CBA) as a tool for evaluating public policies. Students will learn how to interpret and produce CBAs through lectures, problem sets, and real-world case studies focused on environmental, financial, agricultural, and transportation policies. Emphasis is placed on CBAs conducted by government agencies, including critical review of regulatory analyses and formulation of public comments.

This applied course provides students with foundational skills to analyze and interpret publicly available datasets for public policy decision-making. Emphasizing hands-on learning, the course covers data sourcing, cleaning, research design, statistical analysis, and data visualization using Stata. Students will explore real-world challenges across topics such as poverty, education, housing, and public health, culminating in a data-based policy memo developed through collaborative group work.

(Formerly AI Institutions) AI is rewriting the rules of society. This course invites you to understand and shape what comes next. We begin by turning the classroom into a living experiment on how AI could change education, then examine how abundant intelligence could reshape work, governance, and transportation. In a field often dominated by speculation, we will ground our discussions in evidence and theory. Together, we’ll explore what institutions are needed for a world transformed by intelligence. 

This course equips students for humanitarian, human rights, foreign policy and political risk jobs that require real-time interpretation and analysis of conflict data. The course will introduce students to contemporary open-source data about conflict events, fatalities, forced displacement, human rights violations, settlement patterns in war zones, and much more. Students will learn about how this data is generated, what data reveals, what data obscures, and the choices analysts can make to use conflict data transparently in the face of biases.

MIA and MPA Policy Skills II Core. In recent years, despite enhanced awareness about the magnitude and multifaceted nature of gender inequalities on the one hand, and the promises of the ‘Data Revolution’ including AI on the other hand, gaps remain in both data availability and usage of 'Gender Data' that aim to both capture the underlying dynamics, drivers and outcomes of gender inequalities, and promote gender equality.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. Alternate title: "How to Use a Bit of Code to Do Things That Would Be Really Hard in Spreadsheets." Students will learn data analysis through the Python programming language—exploring, manipulating, visualizing, and interpreting open data to answer policy questions. The class incorporates use of generative AI for coding problems, helping students understand its strengths and weaknesses. No coding experience required.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course provides a practical introduction to the core concepts, techniques, and tools used to analyze data for effective decision-making.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This advanced course provides a comprehensive introduction to the principles and practices of effective database design, management, and security. Students will gain a strong foundation in information organization, data storage, and database administration, with attention to key topics such as data warehousing, governance, security, privacy, and alternative database models.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500). Priority Registration: MIA and MPA. Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This 7-week mini-course leads the students into the R world, helps them master the basics, and establishes a platform for future self-study. The course offers students basic programming knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation. Students will learn how to install R and RStudio, understand and use R data objects, and become familiar with base R and several statistical and graphing packages.

MIA and MPA Policy Skills II Core. This course introduces students to foundational concepts and methods for analyzing text-as-data using Python. Designed for beginners with no prior coding experience, the course emphasizes hands-on learning and practical applications across disciplines. Students will explore computational techniques for collecting, cleaning, and analyzing text data from sources such as news media, social media, and websites. Topics include web scraping, working with APIs, sentiment analysis, topic modeling, named entity recognition, and more.

MIA and MPA Policy Skills II Core. Pre-req: Computing in Context (DSPC IA6000). Priority Registration: MIA and MPA. This course introduces students to the fundamentals of Artificial Intelligence (AI), its applications in public policy, and its implications for the future of governance. Students will gain a foundational understanding of AI, including the mathematical and programming principles behind common machine learning algorithms used for prediction, classification, and clustering.

MIA and MPA Policy Skills II Core. This course introduces students to the fundamentals of Generative Artificial Intelligence (Generative AI), with a focus on how these technologies are built and their implications for society and public policy. Students will gain an understanding of language models, large language models (LLMs), deep learning, transformers, and Generative Pre-Trained Transformers (GPT).

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. In the past two years, Large Language Models (LLMs) built using transformer frameworks have emerged as the fastest-growing area of research and investment in AI/machine learning. Recent releases of chatbots such as ChatGPT (OpenAI), Bing (Microsoft), and Bard (Google) quickly reached hundreds of millions of users and have become the face of artificial intelligence for consumers.

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.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. Instructor permission required. Join the waitlist in Vergil to request registration. 

This course bridges the gap between data science and public policy by bringing together students from diverse academic backgrounds to address contemporary policy challenges using large-scale data. With the rapid growth of digital information and the increasing influence of machine learning and AI on public life, the ability to work across disciplines is becoming essential.

Pre-req: SIPA IA6500 - Quant I, and prior experience with R are required. Instructor permission required. Join the waitlist in Vergil to request registration. 

Pre-req: DSPC IA6000 - Computing in Context, or see option for testing out. In Computing in Context, students "explore[d] computing concepts and coding in the context of solving policy problems." Building off that foundation of Python fundamentals and data analysis, Advanced Computing for Policy goes both deeper and broader.

(Formerly AI Institutions) AI is rewriting the rules of society. This course invites you to understand and shape what comes next. We begin by turning the classroom into a living experiment on how AI could change education, then examine how abundant intelligence could reshape work, governance, and transportation. In a field often dominated by speculation, we will ground our discussions in evidence and theory. Together, we’ll explore what institutions are needed for a world transformed by intelligence. 

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.)

This course equips students for humanitarian, human rights, foreign policy and political risk jobs that require real-time interpretation and analysis of conflict data. The course will introduce students to contemporary open-source data about conflict events, fatalities, forced displacement, human rights violations, settlement patterns in war zones, and much more. Students will learn about how this data is generated, what data reveals, what data obscures, and the choices analysts can make to use conflict data transparently in the face of biases.

MIA and MPA Policy Skills II Core. In recent years, despite enhanced awareness about the magnitude and multifaceted nature of gender inequalities on the one hand, and the promises of the ‘Data Revolution’ including AI on the other hand, gaps remain in both data availability and usage of 'Gender Data' that aim to both capture the underlying dynamics, drivers and outcomes of gender inequalities, and promote gender equality.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. Alternate title: "How to Use a Bit of Code to Do Things That Would Be Really Hard in Spreadsheets." Students will learn data analysis through the Python programming language—exploring, manipulating, visualizing, and interpreting open data to answer policy questions. The class incorporates use of generative AI for coding problems, helping students understand its strengths and weaknesses. No coding experience required.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course provides a practical introduction to the core concepts, techniques, and tools used to analyze data for effective decision-making.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This advanced course provides a comprehensive introduction to the principles and practices of effective database design, management, and security. Students will gain a strong foundation in information organization, data storage, and database administration, with attention to key topics such as data warehousing, governance, security, privacy, and alternative database models.

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500). Priority Registration: MIA and MPA. Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This 7-week mini-course leads the students into the R world, helps them master the basics, and establishes a platform for future self-study. The course offers students basic programming knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation. Students will learn how to install R and RStudio, understand and use R data objects, and become familiar with base R and several statistical and graphing packages.

MIA and MPA Policy Skills II Core. This course introduces students to foundational concepts and methods for analyzing text-as-data using Python. Designed for beginners with no prior coding experience, the course emphasizes hands-on learning and practical applications across disciplines. Students will explore computational techniques for collecting, cleaning, and analyzing text data from sources such as news media, social media, and websites. Topics include web scraping, working with APIs, sentiment analysis, topic modeling, named entity recognition, and more.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

MIA and MPA Policy Skills II Core. Pre-req: Computing in Context (DSPC IA6000). Priority Registration: MIA and MPA. This course introduces students to the fundamentals of Artificial Intelligence (AI), its applications in public policy, and its implications for the future of governance. Students will gain a foundational understanding of AI, including the mathematical and programming principles behind common machine learning algorithms used for prediction, classification, and clustering.

MIA and MPA Policy Skills II Core. This course introduces students to the fundamentals of Generative Artificial Intelligence (Generative AI), with a focus on how these technologies are built and their implications for society and public policy. Students will gain an understanding of language models, large language models (LLMs), deep learning, transformers, and Generative Pre-Trained Transformers (GPT).

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. In the past two years, Large Language Models (LLMs) built using transformer frameworks have emerged as the fastest-growing area of research and investment in AI/machine learning. Recent releases of chatbots such as ChatGPT (OpenAI), Bing (Microsoft), and Bard (Google) quickly reached hundreds of millions of users and have become the face of artificial intelligence for consumers.

MPA Quantitative II Core. This course introduces regression analysis as a key tool for policy analysis and program evaluation. Emphasizing causal inference, students will learn to assess the impacts of programs and policies using both experimental and non-experimental methods. The first half of the course reviews foundational concepts from Quant I and builds toward multiple regression techniques; the second half applies those tools to real-world policy settings.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study.

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.

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.)

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. This course applies empirical economic tools to the study of education policy, with a focus on both K-12 and higher education systems. Topics include class size, peer effects, teacher quality, school accountability, school choice, vouchers, and student incentives.

Pre-requisites: A calculus-based micro-economics course (SIPA IA6400) or equivalent. This is an advanced course in development economics, designed for SIPA students interested in rigorous, applied training. Coursework includes extensive empirical exercises, requiring programming in Stata. The treatment of theoretical models presumes knowledge of calculus.

This course will cover practical time series forecasting techniques and consists of two parts. The first part focuses on the Box-Jenkins approach (ARIMA), including identification (selection) of the appropriate model, estimation of its parameters, and diagnostic checking of model adequacy. The second part of the course is on nonlinear models for time series, with emphasis on conditional volatility and ARCH models. By the end of the course, you will be able to apply these techniques to actual data, primarily financial and economic time series. 

Discrimination is the differential treatment of people based on identity or perceived identity (race, gender, ethnicity, LGBTQ+ status, age, religion, disability, immigration status, etc.). Such behavior violates some legal, social, and moral norms and has a negative impact on those discriminated against.

For these and other reasons, it is important to be able to formally identify discrimination from data. But how do we know that A’s treatment of B is because of B’s identity as opposed to some other characteristic of B or A that we may not even have a variable for?

This course provides a rigorous introduction to renewable energy project finance modeling, focusing on the concepts, structures, and financial mechanisms that underpin investment in renewable energy projects such as wind and solar. Through lectures, demonstrations, and guided analysis of actual project documents and contracts, students will develop a comprehensive understanding of the key drivers of renewable energy economics and financing.

This course examines the principles and practices of monitoring and evaluation (M&E) in international development and humanitarian assistance. Students will learn to design theories of change, develop indicators, plan and conduct evaluations, and communicate results effectively. Emphasis is placed on adaptive management, complexity-aware approaches, and emerging trends such as equitable and decolonized evaluation and the integration of generative AI tools.

Pre-requisites: A calculus-based micro-economics course (SIPA IA6400) or equivalent. This is an advanced course in development economics, designed for SIPA students interested in rigorous, applied training. Coursework includes extensive empirical exercises, requiring programming in Stata. The treatment of theoretical models presumes knowledge of calculus.

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500). Priority Registration: MIA and MPA. Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This 7-week mini-course leads the students into the R world, helps them master the basics, and establishes a platform for future self-study. The course offers students basic programming knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation. Students will learn how to install R and RStudio, understand and use R data objects, and become familiar with base R and several statistical and graphing packages.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

This course equips students with the skills and tools to design, assess, and manage impact measurement and evaluation (M&E) strategies within sustainable development and social impact contexts. Emphasizing both technical rigor and real-world application, the course prepares students to develop M&E frameworks, apply theories of change, track and evaluate outcomes, and communicate findings to diverse audiences.

This applied course provides students with foundational skills to analyze and interpret publicly available datasets for public policy decision-making. Emphasizing hands-on learning, the course covers data sourcing, cleaning, research design, statistical analysis, and data visualization using Stata. Students will explore real-world challenges across topics such as poverty, education, housing, and public health, culminating in a data-based policy memo developed through collaborative group work.

MPA Quantitative II Core. This course introduces regression analysis as a key tool for policy analysis and program evaluation. Emphasizing causal inference, students will learn to assess the impacts of programs and policies using both experimental and non-experimental methods. The first half of the course reviews foundational concepts from Quant I and builds toward multiple regression techniques; the second half applies those tools to real-world policy settings.

Pre-req: SIPA IA6501 - Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study.

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.

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500). Priority Registration: MIA and MPA. Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study.

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.

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.)

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. This course applies empirical economic tools to the study of education policy, with a focus on both K-12 and higher education systems. Topics include class size, peer effects, teacher quality, school accountability, school choice, vouchers, and student incentives.

Pre-requisites: A calculus-based micro-economics course (SIPA IA6400) or equivalent. This is an advanced course in development economics, designed for SIPA students interested in rigorous, applied training. Coursework includes extensive empirical exercises, requiring programming in Stata. The treatment of theoretical models presumes knowledge of calculus.

This course will cover practical time series forecasting techniques and consists of two parts. The first part focuses on the Box-Jenkins approach (ARIMA), including identification (selection) of the appropriate model, estimation of its parameters, and diagnostic checking of model adequacy. The second part of the course is on nonlinear models for time series, with emphasis on conditional volatility and ARCH models. By the end of the course, you will be able to apply these techniques to actual data, primarily financial and economic time series. 

Discrimination is the differential treatment of people based on identity or perceived identity (race, gender, ethnicity, LGBTQ+ status, age, religion, disability, immigration status, etc.). Such behavior violates some legal, social, and moral norms and has a negative impact on those discriminated against.

For these and other reasons, it is important to be able to formally identify discrimination from data. But how do we know that A’s treatment of B is because of B’s identity as opposed to some other characteristic of B or A that we may not even have a variable for?

This course provides a rigorous introduction to renewable energy project finance modeling, focusing on the concepts, structures, and financial mechanisms that underpin investment in renewable energy projects such as wind and solar. Through lectures, demonstrations, and guided analysis of actual project documents and contracts, students will develop a comprehensive understanding of the key drivers of renewable energy economics and financing.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This 7-week mini-course leads the students into the R world, helps them master the basics, and establishes a platform for future self-study. The course offers students basic programming knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation. Students will learn how to install R and RStudio, understand and use R data objects, and become familiar with base R and several statistical and graphing packages.

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

This course equips students with the skills and tools to design, assess, and manage impact measurement and evaluation (M&E) strategies within sustainable development and social impact contexts. Emphasizing both technical rigor and real-world application, the course prepares students to develop M&E frameworks, apply theories of change, track and evaluate outcomes, and communicate findings to diverse audiences.

This course provides an applied introduction to cost-benefit analysis (CBA) as a tool for evaluating public policies. Students will learn how to interpret and produce CBAs through lectures, problem sets, and real-world case studies focused on environmental, financial, agricultural, and transportation policies. Emphasis is placed on CBAs conducted by government agencies, including critical review of regulatory analyses and formulation of public comments.

This applied course provides students with foundational skills to analyze and interpret publicly available datasets for public policy decision-making. Emphasizing hands-on learning, the course covers data sourcing, cleaning, research design, statistical analysis, and data visualization using Stata. Students will explore real-world challenges across topics such as poverty, education, housing, and public health, culminating in a data-based policy memo developed through collaborative group work.

MPA Quantitative II Core. This course introduces regression analysis as a key tool for policy analysis and program evaluation. Emphasizing causal inference, students will learn to assess the impacts of programs and policies using both experimental and non-experimental methods. The first half of the course reviews foundational concepts from Quant I and builds toward multiple regression techniques; the second half applies those tools to real-world policy settings.

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.

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.

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

The spread of information technology has led to the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced form causal inference, structural modeling) to work on real world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. Instructor permission required. Join the waitlist in Vergil to request registration. 

This course bridges the gap between data science and public policy by bringing together students from diverse academic backgrounds to address contemporary policy challenges using large-scale data. With the rapid growth of digital information and the increasing influence of machine learning and AI on public life, the ability to work across disciplines is becoming essential.

This course will bridge the gap between data science and public policy in several exciting ways. By drawing on a diverse student body – consisting of students from SIPA and the Data Science Institute – we will combine domain-level policy expertise with quantitative analytical skills as we work on cutting-edge policy problems with large amounts of data.

Pre-req: SIPA IA6500 - Quant I, and prior experience with R are required. Instructor permission required. Join the waitlist in Vergil to request registration. 

This course is an introduction to the quantitative analysis of text as data – a rapidly growing field within the social sciences. The availability of textual data has grown massively in recent years, and so has the demand for skills to analyze it. Vast amounts of digital content are becoming increasingly relevant to various policy-relevant questions.

Pre-req: DSPC IA6000 - Computing in Context, or see option for testing out. In Computing in Context, students "explore[d] computing concepts and coding in the context of solving policy problems." Building off that foundation of Python fundamentals and data analysis, Advanced Computing for Policy goes both deeper and broader.

Pre-req: INAF U6006 - Computing in Context, or see option for testing out.  In Computing in Context, students “explore[d] computing concepts and coding in the context of solving policy problems.” Building off that foundation of Python fundamentals and data analysis, Advanced Computing for Policy goes both deeper and broader.

Pre-req: SIPA IA6501 - Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study.

Pre-req: Quant II. The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form Did A cause B? using data that do not conform to a perfectly controlled randomized study.

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.

Prerequisite Course: SIPAU6501 - Quantitative Analysis II. The goal of this course is to enable students to evaluate the policy relevance of academic research. While academic research frequently considers treatments that approximate a potential public policy, such prima facie relevance alone does not inform policy. In particular, public policy is predicated on the credible estimation of causal treatment effects.

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.)

Prerequisites: Instructor-Managed Waitlist, Course Application, and Quantitative Analysis II. This course will develop the skills to prepare, analyze, and present data for policy analysis and program evaluation using R. In Quant I and II, students are introduced to probability and statistics, regression analysis and causal inference. In this course we focus on the practical application of these skills to explore data and policy questions on your own.

Pre-req: SIPA IA6501 - Quant II or equivalent quantitative methods course. This course applies empirical economic tools to the study of education policy, with a focus on both K-12 and higher education systems. Topics include class size, peer effects, teacher quality, school accountability, school choice, vouchers, and student incentives.

This course explores the central themes in K-12 and higher education from an economic perspective. Topics in K-12 education include the effects of class-size, peer effects, teachers, accountability, charter schools, and vouchers. Topics in higher education include the decision to invest in human capital, estimating returns to college, and the market for college education. The course will cover research and policy issues from both domestic and international contexts.

Pre-req: any Quant III course. Instructor Managed Registration.

Pre-req: any Quant III course. Instructor Managed Registration.

Pre-requisites: A calculus-based micro-economics course (SIPA IA6400) or equivalent. This is an advanced course in development economics, designed for SIPA students interested in rigorous, applied training. Coursework includes extensive empirical exercises, requiring programming in Stata. The treatment of theoretical models presumes knowledge of calculus.