Event Studies with Feedback

TL;DR

This paper introduces a dynamic panel event study framework that disentangles direct and indirect effects, accounting for feedback mechanisms and heterogeneity in treatment effects, enhancing causal inference in observational studies.

Contribution

It develops a novel methodology to separate direct and feedback effects in event studies, allowing for persistent outcomes and endogenous covariates, with point identification under certain assumptions.

Findings

Framework successfully separates direct and feedback effects.

Algorithm enables dynamic decomposition of treatment effects.

Theoretical results establish identification of key parameters.

Abstract

Event studies often conflate direct treatment effects with indirect effects operating through endogenous covariate adjustment. We develop a dynamic panel event study framework that separates these effects. The framework allows for persistent outcomes and treatment effects and for covariates that respond to past outcomes and treatment exposure. Under sequential exogeneity and homogeneous feedback, we establish point identification of common parameters governing outcome and treatment effect dynamics, the distribution of heterogeneous treatment effects, and the covariate feedback process. We propose an algorithm for dynamic decomposition that enables researchers to assess the relative importance of each effect in driving treatment effect dynamics.