Summer 2026 Causal Inference Intensive Course by Associate Professor Yiqing Xu, Stanford University@ Waseda Graduate School of Political Science

Panel data are the most common basis for causal claims in the social sciences. For decades the default analysis was a two-way fixed effects regression. Over the past ten years that default has been challenged, and dozens of alternative estimators have appeared. This short course works through the change, from the classical models to the methods now in use.

The first half covers the parametric panel models and what their assumptions require, then difference-in-differences as a research design. It closes with the result that two-way fixed effects recover a weighted average of treatment effects whose weights can turn negative when units are treated at different times, as well as other pathologies. The second half covers the responses: heterogeneity-robust DID estimators, factorial DID, the synthetic control method and latent factor models, and design-based approaches that move the assumptions from the outcome model to the treatment assignment process.

Each lecture pairs the identification argument with applied examples and diagnostics: what to plot, what to test, and what to report when the assumptions are in doubt. Implementation is in R, using panelView, fect, and related packages. Participants should have taken a graduate or advanced-undergraduate level regression course. Prior experience with R is helpful but not required.

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