🤖 AI Summary
This study addresses the bias in traditional ATT estimation arising from parallel trends violations in subsets of the treatment group by proposing a Credible Subgroup Local ATT. This method achieves robust causal identification under weak parallel trends assumptions through reweighting and honest sensitivity bounds, point-identifying a novel estimand based solely on cohort-specific parallel trends at the cost of a narrowed estimation scope. Simulations confirm the method’s validity, while an empirical application demonstrates that the previously documented positive effect of the shale gas boom on housing prices is actually driven by trend bias rather than genuine causality. By revealing no true causal impact through credible subgroup analysis, these findings underscore the approach’s critical value in correcting selection bias when standard identifying assumptions are compromised.
📝 Abstract
When parallel trends fails for some treated cohorts but not others, the average treatment effect on the treated (ATT), an average over all of them, is exactly the target that becomes hard to recover. We propose changing the estimand rather than defending it. The credible-subpopulation local ATT (LATT) is the effect for the subpopulation of cohorts whose parallel trends is credible, and it is point-identified under parallel trends for the selected cohorts alone, a weaker requirement that can hold when the ATT's fails. It is estimated by reweighting standard group-time effects toward those cohorts, and paired with honest sensitivity bounds on the residual violation that a pre-trend screen cannot rule out. The method's advantage grows with how informative pre-trends are about post-treatment violations, as simulations confirm. In an application, a significantly positive pooled estimate of the shale boom's effect on local house prices proves to rest on cohorts already trending before onset, and the credible subpopulation reveals no effect.