Heterogeneous Treatment Effects via Dynamic Panel Data Methods

Philip Marx, Elie Tamer and Xun Tang

Papers from arXiv.org

Abstract: We study dynamic panel data methods in a causal framework where treatment effects are heterogeneous and history-dependent. Sequential exchangeability alone does not guarantee that a first-differenced instrumental-variable (IV) estimand is a convex average of current effects, even if those effects are constant. We characterize additional conditions that are sufficient and necessary for eliminating trend contamination and guaranteeing a convex representation uniformly over compatible distributions of potential outcomes and treatments. Next, for a model with autoregressive untreated potential outcomes, we propose a conditional first-differenced IV regression that identifies the average treatment effect on the first-time treated without sequential exchangeability. Finally, we compare sequential exchangeability and parallel trends under sequential randomization and forward-looking treatment choices with learning, and show that, when treatments depend on recent outcomes, equal pre-treatment trends can coexist with violations of parallel trends.

Date: 2024-10, Revised 2026-11
New Economics Papers: this item is included in nep-ecm
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