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.

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.

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?

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.

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 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.