A Difference-in-Differences Estimator for Causal Inference with TSCS Data

• In Event: Beyond Traditional DiD: Exploring New Approaches in Panel Data Analysis

Abstract

This paper addresses the challenge of deriving robust causal effects from time-series cross-sectional (TSCS) data. The task is especially complex with multiple treatment status changes, heterogeneous treatment effects, and unobserved time-varying confounders, leading to increased bias and reduced efficiency. Here, we introduce a novel difference-in-differences (DID) estimator to assess the average treatment effect on the treated (ATT), building upon the principles of doubly robust DID estimation. Our approach involves creating matched sets by pairing each treated observation with control observations from different groups that share an identical treatment history. We then employ a combination of propensity score and outcome regression methods, incorporating machine learning algorithms with cross-validation, to calculate both immediate and long-term ATTs. Our simulation and empirical analyses demonstrate the estimator's semi parametric efficiency and resilience to incorrect model specifications. We also introduce an open-source software package for these methods' implementation.

Authors

• Yohan Park, Trinity College Dublin

• Thomas Chadefaux, Trinity College Dublin