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Machine Learning-Augmented Sensitivity Analysis for Causality

Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.

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Machine Learning-Augmented Sensitivity Analysis for Causal Inference

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