Counterpart Statistics in the Matched Difference-in-Differences Design

Music City Center 

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.

Difference-in-differences

Matching

Non-equivalent control

Measurement error

Health policy evaluation

Triple difference 

Health Policy Statistics Section