Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
arXiv 26 Dec 2021 · Econometrics · 22 citations (OpenAlex)
arXiv:2112.13398 · PDF · DOI · OpenAlex · Extracted main text
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts. Our theory applies to nonparametric models, while naturally allowing for (semi-)parametric restrictions (such as partial linearity) when such assumptions are made. We show how simple plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the magnitude of the bias, thus facilitating sensitivity analysis in otherwise complex, nonlinear models. Finally, we provide flexible and efficient statistical inference methods for the bounds, which can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple, and interpretable, tools. We demonstrate the utility of our approach with two empirical examples.
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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 | Judea Pearl (2009) Causality | 1.000 | 5 | 3 | 100% |
| 2 | Victor Chernozhukov, Whitney Newey, and Rahul Singh (1802) De-biased machine learning of global and local parameters using regularized riesz representers self | 0.928 | 5 | 4 | 80% |
| 3 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects self | 0.928 | 5 | 3 | 80% |
| 4 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2016) Double/debiased machine learning for treatment and structural parameters self | 0.923 | 14 | 5 | 79% |
| 5 | Guido W Imbens (2003) Sensitivity to exogeneity assumptions in program evaluation | 0.843 | 5 | 5 | 60% |
| 6 | Paul R Rosenbaum (2002) Observational studies | 0.843 | 5 | 3 | 60% |
| 7 | Zhiqiang Tan (2006) A distributional approach for causal inference using propensity scores | 0.843 | 5 | 3 | 60% |
| 8 | Joshua D. Angrist and Jorn-Steffan Pischke (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.811 | 4 | 2 | 100% |
| 9 | Carlos Cinelli and Chad Hazlett (2020) Making sense of sensitivity: Extending omitted variable bias self | 0.794 | 6 | 5 | 50% |
| 10 | Victor Chernozhukov, Whitney Newey, Rahul Singh, and Vasilis Syrgkanis (2020) Adversarial estimation of riesz representers self | 0.737 | 3 | 2 | 100% |
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