Confounding and Regression Adjustment in Difference-in-Differences
Abstract
Difference-in-differences (diff-in-diff) is a study design that compares outcomes of two groups (treated and comparison) at two time points (pre- and post-treatment) and is widely used in evaluating new policy implementations. For instance, diff-in-diff has been used to estimate the effect that increasing minimum wage has on employment rates and to assess the Affordable Care Act's effect on health outcomes. Although diff-in-diff appears simple, potential pitfalls lurk. In this paper, we discuss one such complication: time-varying confounding. We provide rigorous definitions for confounders in diff-in-diff studies and explore regression strategies to adjust for confounding. In simulations, we show how and when regression adjustment can ameliorate confounding for both time-invariant and time-varying covariates. We compare our regression approach to those models commonly fit in applied literature, which often fail to address the time-varying nature of confounding in diff-in-diff.
Cite
@article{arxiv.1911.12185,
title = {Confounding and Regression Adjustment in Difference-in-Differences},
author = {Bret Zeldow and Laura A. Hatfield},
journal= {arXiv preprint arXiv:1911.12185},
year = {2019}
}