arXiv 4 Apr 2026 · Econometrics
arXiv:2604.03544 · PDF · DOI · OpenAlex · Extracted main text
We develop a framework for quantifying omitted variable bias (OVB) in nonlinear instrumental variable (IV) estimators, including the local average treatment effect (LATE), the LATE for the treated (LATT), and the partially linear IV model (PLIVM). Extending sensitivity analysis beyond linear settings, we derive bias decompositions, establish partial identification bounds, and construct OVB-adjusted confidence intervals. We estimate OVB bounds and conduct inference using double machine learning (DML), allowing flexible control for high-dimensional covariates. An application to the U.S. Job Training Partnership Act (JTPA) experiment shows that, at conventional significance levels, first-stage compliance estimates are robust to omitted variables, whereas intention-to-treat and treatment effects are more sensitive. Program impacts are robust and significant for females but fragile for males.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Victor Chernozhukov and Carlos Cinelli and Whitney Newey and Amit Sh… (2024) Long Story Short: Omitted Variable Bias in Causal Machine Learning | 0.928 | 10 | 6 | 80% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 10 | 3 | 80% |
| 3 | Stoye, Jörg (2009) More on Confidence Intervals for Partially Identified Parameters | 0.874 | 7 | 2 | 100% |
| 4 | Imbens, Guido W. and Manski, Charles F (2004) Confidence Intervals for Partially Identified Parameters | 0.737 | 4 | 2 | 75% |
| 5 | Abadie, Alberto and Angrist, Joshua and Imbens, Guido (2002) Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings | 0.585 | 3 | 1 | 100% |
| 6 | Victor Chernozhukov and Christian Hansen and Nathan Kallus and Marti… (2024) Applied Causal Inference Powered by ML and AI | 0.405 | 1 | 1 | 100% |
| 7 | Carlos Cinelli and Chad Hazlett (2022) An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables | 0.405 | 1 | 1 | 100% |
| 8 | Cinelli, Carlos and Hazlett, Chad (2025) An omitted variable bias framework for sensitivity analysis of instrumental variables | 0.405 | 1 | 1 | 100% |
| 9 | Markus Frölich (2007) Nonparametric IV estimation of local average treatment effects with covariates | 0.405 | 1 | 1 | 100% |
| 10 | Jinyong Hahn (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 11 scored citations.