Our flagship Causal Inference Series.
This two-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.
Courses MC'd by leading scholars.
These shorter courses are taught by a leading researcher focusing on specific topics.
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.
• Starting Nov 2nd
Shift-Share Instrumental Variables (SSIV) are used to address endogeneity and selection challenges in many economic settings. This half-day workshop 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.
• Starting September 27th
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.
• Starting October 28th
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.