“The Classics”

Our flagship Causal Inference Series.

This three-part series is designed to survey the large and complicated field of causal inference following the structure of Scott Cunninghams's book, Causal Inference: The Mixtape. We will review the theory behind each of these research designs in detail with the aim being comprehension, competency and confidence.

Causal Inference I

Prof. Scott Cunningham

Starting September 9th

All course material is available free and open source via our Github Repository .

  • Potential Outcomes

  • Matching Methods

  • Instrumental Variables

  • Regression Discontinuity

Causal Inference II

Prof. Scott Cunningham

Starting October 14th

All course material is available free and open source via our Github Repository .

  • Difference-in-Differences

  • Advanced Diff-in-Diff

  • Fuzzy Diff-in-Diff

Causal Inference III

Prof. Scott Cunningham

Starting November 11th

All course material is available free and open source via our Github Repository .

  • Synthetic Control

  • Multiple Treated Units

“The Singles”

Approachable Introductions to Specific Methods

Discover the foundations of fascinating subjects with our highly accessible workshop series, "The Singles." Led by esteemed scholars, these workshops provide comprehensive introductions to specific topics and methods. Designed for beginners, each session starts from the basics, equipping you with a solid understanding of the subject matter. Embark on a transformative learning journey with our expert instructors, who will guide you through the essentials and ignite your passion for the subject.

Regression Discontinuity Design

Prof. Rocío Titiunik

Starting October 3rd, 2023

This course covers methods for the analysis and interpretation of the Regression Discontinuity (RD) design, a non-experimental strategy to study treatment effects that can be used when units receive a treatment based on a score and a cutoff. The course covers methods for estimation, inference, and falsification of RD treatment effects using two different approaches: the continuity-based framework, implemented with local polynomials, and the local randomization framework, implemented with standard tools from the analysis of experiments. The focus is on conceptual understanding of the underlying methodological issues and effective empirical implementation. Every topic is illustrated with the analysis of RD examples using real-world data, walking through R and Stata codes that fully implement all the methods discussed. At the end of the course, participants will have acquired the necessary skills to rigorously interpret, visualize, validate, estimate, and characterize the uncertainty of RD treatment effects.

All course material is available free and open source via our Github Repository .

Doing Applied Research

Prof. Daniel Rees and Prof. D. Mark Anderson

Starting October 26th, 2023

This course is intended to be a practical guide for graduate students and early career economists doing applied research. The nuts and bolts of writing, publishing, and service to the profession are covered in two half-day sessions, each lasting roughly four hours (including short breaks). We begin by providing tips on how to start a research project, when to switch topics, and how to effectively manage multiple projects at once. Next, we provide practical advice on how to write an applied economics paper, from structing the introduction to crafting the conclusion. The second half of the course takes participants through the publication process. In addition, we discuss networking, refereeing for economics journals, getting the most out of conferences, and how to successfully navigate the academic job market.

All course material is available free and open source via our Github Repository .

Machine Learning and Causal Inference

Prof. Brigham Frandsen

Starting October 30th, 2023

Machine Learning's wheelhouse is out-of-sample prediction, but these powerful methods can be deployed in service of causal inference. This two-session workshop will introduce the basics of machine learning prediction methods, including lasso and random forests and how they feature in causal inference methods like double machine learning (DML) and post-double selection lasso (PDS lasso). The course covers the conceptual and theoretical basis for the methods and also gets into the nuts and bolts of implementation in python and Stata using real-world data.

All course material is available free and open source via our Github Repository .

Empirical Bayes and Large-Scale Inference

Prof. Christopher Walters

Starting November 6th, 2023

This mixtape session will cover Empirical Bayes methods for studying heterogeneity, estimating individual effects, and making decisions in settings with many unit-specific parameters. Examples include studies of school, teacher, and physician quality; neighborhood effects on economic mobility; firm effects on wages; employer-specific labor market discrimination; and individualized treatment effect predictions and policy recommendations. Topics will include methods for quantifying variation in effects, empirical Bayes shrinkage, connections to machine learning methods, and large-scale inference tools for multiple testing and decision-making. By the end of the course, participants will be equipped to utilize these methods in their own research or business applications.

All course material is available free and open source via our Github Repository .

Instrumental Variables

Prof. Peter Hull

Not currently scheduled

Instrumental variables (IV) is a powerful tool for leveraging external ("exogenous") variation to estimate the causal effects of otherwise confounded ("endogenous") variables. This two-day workshop will introduce the basics of IV through different practical examples, formalize the requirements of a valid and powerful IV, and discuss the mechanics of the two-stage least squares (2SLS) estimator. Special focus will be paid on interpreting linear IV under heterogeneous treatment effects and recent advances in judge leniency designs. The course will include substantial group programming exercises, where different IV techniques will be illustrated in real-world applications.

All course material is available free and open source via our Github Repository .

Synthetic Control and Clustering

Prof. Alberto Abadie

Not currently scheduled

In this course, we will cover the fundamentals of synthetic control estimation and inference, with special emphasis on actionable guidance for applied research. We will discuss seven crucial guiding principles for empirical studies using synthetic controls and how these principles are applied in practice. Towards the end of the course, we will change topics to address “the” FAQ of econometrics office hours: When and how should we cluster standard errors?

All course material is available free and open source via our Github Repository .

“The Deep Cuts”

Advanced Courses Exploring the Frontiers of Causal Inference

Unleash the power of your existing knowledge with our advanced workshop series, "The Deep Cuts." Perfect for those well-versed in the basics of a methodology, these immersive courses take you on a captivating exploration into the cutting-edge of your chosen topic. Led by leading scholars, each session delves deep into the frontiers of a methodology. These courses are designed assuming a solid foundation in the basics of the methodology.

Shift-Share IV

Prof. Peter Hull

Starting September 25th, 2023

Shift-Share Instrumental Variables (SSIV) are used to address endogeneity and selection challenges in many economic settings. This workshop will last two evenings and will introduce the basics of SSIV and cover the recent literature on its econometric foundations. Special focus will be paid on the different assumptions underlying the “exogenous shares” and “exogenous shocks” approaches to SSIV identification, and their practical implications. We will also cover a more general class of instrumental variable strategies combining exogenous shocks and non-random exposure. Group programming exercises will be used to illustrate various theoretical concepts in real-world applications.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of instrumental variables and will cover the frontiers of the topic. A good review is the intro course: https://github.com/Mixtape-Sessions/Instrumental-Variables.

All course material is available free and open source via our Github Repository .

Frontiers in DID

Prof. Brantly Callaway

Starting October 17th, 2023

This course provides an in-depth introduction to panel data approaches to causal inference. The first part of the course reviews how new "heterogeneity-robust" estimation strategies address some important limitations of traditional two-way fixed effects regressions in difference-in-differences applications, and then provides an in-depth discussion/comparison of many of these approaches. This part also includes a number of practical extensions such as how to include covariates in the parallel trends assumption and dealing with "bad controls". The second part of the course discusses how the insights of recent work on difference-in-differences can apply in a number of other settings that frequently arise in empirical work. And, in particular, this part of the course provides connections between the difference-in-differences literature and alternative identification strategies (conditioning on lagged outcomes, change-in-changes, and interactive fixed effects models) and also how to deal with more complicated treatment regimes (continuous treatments or treatments that can change value over time).

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of the difference-in-differences methodology and will cover the frontiers of the topic. A good review is: https://github.com/Mixtape-Sessions/Causal-Inference-2.

All course material is available free and open source via our Github Repository .

Machine Learning and Heterogeneous Effects

Prof. Brigham Frandsen

Starting November 15th, 2023

The holy grail of causal inference is the individual-level treatment effect: how would a particular patient respond to a drug? Which users will respond most to a targeted ad? Would a given student be helped or harmed by a classroom intervention? This session introduces machine learning tools for estimating heterogeneous treatment effects like random causal forests. The course goes over the theory and concepts as well as the nitty-gritty of coding the methods up in python, R, and Stata using real-world examples. This course can be taken as a follow-up to the Machine Learning and Causal Inference mixtape session, or as a stand-alone course.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of machine learning and causal inference and will cover the frontiers of the topic. A good review is the intro course: https://github.com/Mixtape-Sessions/Machine-Learning.

All course material is available free and open source via our Github Repository .

Design-Based Inference

Prof. Peter Hull

Starting November 27th, 2023

This three-day workshop covers a wide range of practical results for regression and IV-based analyses of causal effects which leverage random or conditionally as-good-as-random shocks. Questions of particular focus include:
  • "What controls do I need to include to avoid omitted variables bias?"
  • "Do I need to worry about 'negative weighting' of heterogeneous effects?"
  • "How should I be clustering my standard errors?"
  • "What's the payoff to considering nonlinear/'structural' analyses?"
Results will be illustrated through several real-world applications.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of causal inference and will cover advanced methods. A solid understanding of the material covered in the material from Scott's courses (Part 1 and Part 2) will be assumed.

All course material is available free and open source via our Github Repository .

Advanced DID

Prof. Jonathan Roth

Not currently scheduled

This one-day workshop will cover advanced topics from the recent difference-in-differences literature. One question of particular focus will be, "what should I do if I'm not 100% sure about the validity of the parallel trends assumption?" We will cover a variety of relaxations to the parallel trends assumption, and new tools for power calculations and sensitivity analysis. The workshop will focus not just on the theory, but also on practical implementation in statistical software such as R and Stata.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of the difference-in-differences methodology and will cover the frontiers of the topic. A good review is: https://github.com/Mixtape-Sessions/Causal-Inference-2.

All course material is available free and open source via our Github Repository .