Long Story Short: Omitted Variable Bias in Causal Machine Learning

• URL: http://arxiv.org/abs/2112.13398v5
• Date: Sun, 26 May 2024 18:43:02 GMT
• Title: Long Story Short: Omitted Variable Bias in Causal Machine Learning
• Authors: Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis,
• Abstract summary: We develop a theory of omitted variable bias for a wide range of common causal parameters. We show how simple plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the magnitude of the bias. We provide flexible and efficient statistical inference methods for the bounds, which can leverage modern machine learning algorithms for estimation.
• Score: 26.60315380737132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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