Estimating Treatment Effects in Panel Data Without Parallel Trends
Abstract
This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups.
Cite
@article{arxiv.2601.08281,
title = {Estimating Treatment Effects in Panel Data Without Parallel Trends},
author = {Shoya Ishimaru},
journal= {arXiv preprint arXiv:2601.08281},
year = {2026}
}