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Bayesian Robustness Values for Modern Causal Panel Estimators via Riesz Representations

Makoto Nakakita, Takahiro Hoshino

arXiv 11 Jul 2026 · Statistics — Methodology

arXiv:2607.10276 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial $R^2$ values. Route B treats observed-covariate benchmarks as auxiliary data only when benchmark-count, alpha-side alignment, model-check, dependence, and dominance diagnostics are credible; otherwise its main role is demotion. We derive estimator-specific Riesz diagnostics and clarify which are fixed-weight, target-level, or first-stage-conditional rather than full derivatives of regularized training maps. Monte Carlo stress tests distinguish calibrated benchmark settings from dominance failure, coarse alpha-side benchmarks, benchmark dependence, noisy covariates, and concentrated SDID weights. In the California tobacco-control panel, the SDID estimate is $-15.60$ packs per capita; corrected finite-donor placebo inference gives standard error 9.49 and add-one $p=0.051$. A refit-weight finite-difference audit changes the Route A nullification robustness value from 0.054 to 0.045, leaving the low-single-digit conclusion unchanged. A county-level minimum-wage application applies the same profile to a multi-cohort staggered panel.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bach, P. and Klaassen, S. and Kueck, J. and Mattes, M. and Spindler, M (2025) Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models Using Riesz Representation0.81142100%
2Callaway, B. and Sant'Anna, P. H. C (2021) Difference-in-Differences With Multiple Time Periods0.7374450%
3Arkhangelsky, D. and Athey, S. and Hirshberg, D. A. and Imbens, G. W… (2021) Synthetic Difference-in-Differences0.7373367%
4Borusyak, K. and Jaravel, X. and Spiess, J (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation0.7373367%
5Cinelli, C. and Hazlett, C (2020) Making Sense of Sensitivity: Extending Omitted Variable Bias0.6443267%
6Chernozhukov, V. and Chetverikov, D. and Demirer, M. and Duflo, E. a… (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters0.64422100%
7Wainstein, L. and Hazlett, C (2025) Sensitivity of Weighted Least Squares Estimators to Omitted Variables0.64422100%
8Abadie, A. and Diamond, A. and Hainmueller, J (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program0.5853333%
9Athey, S. and Bayati, M. and Doudchenko, N. and Imbens, G. and Khosr… (2021) Matrix Completion Methods for Causal Panel Data Models0.5112250%
10Sant'Anna, P. H. C. and Zhao, J (2020) Doubly Robust Difference-in-Differences Estimators0.51121100%

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