A note on sensitivity analysis for post-machine learning causal inference

• In Moosavi et al. (2022) a sensitivity analysis method to unobserved confounding was proposed when estimating an average causal effect with a double robust estimator in high dimensional situations. For this purpose, it was assumed that linear models could sparselyapproximate the nuisance functions (treatment assignment and outcome models). In this note, we relax these assumptions making the sensitivity analysis more generally applicable, for instance when nuisance functions are (weakly) consistently estimated with machine learning algorithms. Simulations and a case study illustrate the performance and use of the method.

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NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

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