Primary Submission Category: Difference in Differences, Synthetic Control, Methods for Panel and Longitudinal Data
Counterpart Statistics in the Matched Difference-in-Differences Design
Authors: Sean Tomlin,
Presenting Author: Sean Tomlin*
Difference-in-differences (DiD) estimates intervention effects under the parallel trends assumption, but nuisance trends can bias estimates. Matching methods that balance pre-intervention trends have been used, yet we show they fail to adjust for latent confounders and introduce regression to the mean bias. Instead, we advocate for methods grounded in explicit causal assumptions about selection bias. We also propose a Bayesian approach to assess parallel trends, avoiding the challenges of specifying non-inferiority thresholds. We demonstrate our method using Medical Expenditure Panel Survey data to estimate the impact of health insurance on healthcare utilization.