causal-inference — papers

EconomicsJournal of Econometrics · Dec 2021 Open access

Difference-in-Differences with Multiple Time Periods

Brantly Callaway and Pedro H. C. Sant'Anna

The paper develops a framework for difference-in-differences designs in which units adopt treatment at different times across multiple periods. It defines group-time average treatment effects and shows how to identify and estimate them under conditional parallel trends, then aggregate them into interpretable summary parameters. The authors provide valid simultaneous inference and apply the methods to estimating the effect of minimum wage increases on teen employment.

difference-in-differences staggered-treatment causal-inference parallel-trends

EconomicsThe Econometrics Journal · Jan 2018 Open access

Double/Debiased Machine Learning for Treatment and Structural Parameters

Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, et al.

The paper develops a general framework for estimating low-dimensional treatment or structural parameters when high-dimensional nuisance components are estimated with machine learning methods. By combining Neyman-orthogonal (debiased) moment conditions with sample-splitting/cross-fitting, the approach removes regularization and overfitting biases. The resulting estimators are root-N consistent, asymptotically normal, and valid for inference despite slowly converging nuisance estimates.

machine-learning causal-inference neyman-orthogonality cross-fitting