Event Studies with Feedback

2026-01-09

AI Summary

This study addresses the frequent conflation in event studies between the direct effect of a treatment and indirect effects arising from adjustments through endogenous covariates. The authors develop a dynamic panel event study framework that, under assumptions of sequential exogeneity and homogeneous feedback, achieves point identification of key parameters governing outcome dynamics, the distribution of heterogeneous treatment effects, and the covariate feedback process. They further propose a dynamic decomposition algorithm to quantify the relative contributions of direct and indirect effects. By explicitly modeling persistence in treatment effects and allowing covariates to respond to both past outcomes and treatment exposure, the method cleanly disentangles direct from indirect causal pathways, offering a more precise tool for causal inference in event study settings.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• event studies
• treatment effects
• endogenous covariates
• feedback
• dynamic panel

Innovation

• dynamic panel event study
• treatment effect decomposition
• endogenous covariates
• sequential exogeneity
• heterogeneous treatment effects