Bias-robust causal inference for panel data
Abstract
We develop a bias-robust framework for causal inference in panel data models with interactive fixed effects. Conventional imputation-based estimators of average treatment effects on the treated do not explicitly account for counterfactual approximation error, potentially leading to misleading inference. Building on bias-aware minimax methods, we construct an estimator that corrects imputed counterfactuals using weighted untreated residuals and delivers confidence intervals with an explicit allowance for the remaining approximation bias. The resulting framework separates protection against approximation bias from the efficiency gains obtained through optimal correction. In simulations, the correction reduces confidence interval length by approximately 13\% relative to bias-aware inference without correction, while preserving empirical coverage. An application to Election Day Registration demonstrates how the correction strengthens the robustness of substantive conclusions to counterfactual error. The proposed approach provides a transparent sensitivity analysis for causal effects when the accuracy of counterfactual imputation cannot be established from observed data alone.
Related
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BibTeX
@misc{alexopoulos2026biasrobust,
title = {{Bias-robust causal inference for panel data}},
author = {Angelos Alexopoulos},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.09837},
eprint = {2608.09837},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.09837},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bias-robust causal inference for panel data
AU - Angelos Alexopoulos
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2608.09837
UR - https://arxiv.org/abs/2608.09837
AB - We develop a bias-robust framework for causal inference in panel data models with interactive fixed effects. Conventional imputation-based estimators of average treatment effects on the treated do not explicitly account for counterfactual approximation error, potentially leading to misleading inference. Building on bias-aware minimax methods, we construct an estimator that corrects imputed counterfactuals using weighted untreated residuals and delivers confidence intervals with an explicit allowance for the remaining approximation bias. The resulting framework separates protection against approximation bias from the efficiency gains obtained through optimal correction. In simulations, the correction reduces confidence interval length by approximately 13\% relative to bias-aware inference without correction, while preserving empirical coverage. An application to Election Day Registration demonstrates how the correction strengthens the robustness of substantive conclusions to counterfactual error. The proposed approach provides a transparent sensitivity analysis for causal effects when the accuracy of counterfactual imputation cannot be established from observed data alone.
ER -