Valid causal inference: model selection and sensitivity to unobserved confounding in high-dimensional settings

• Recently, various methods have been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data generating processes, when high-dimensional nuisance models are estimated by post-model selection or machine learning estimators. These methods typically require that all the confounders are observed to ensure identification of the effects. We contribute by showing how valid semiparametric inference can be obtained in the presence of unobserved confounders and high-dimensional nuisance models. We propose uncertainty intervals which allow for unobserved confounding, and show that the resulting inference is valid when the amount of unobserved confounding is small relative to the sample size; the latter is formalized in terms of convergence rates. Simulation experiments illustrate the finite sample properties of the proposed intervals and investigate an alternative procedure that improves the empirical coverage of the intervals when the amount of unobserved confounding is large.

Ämnesord

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

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

Nyckelord

Average treatment effect

Inverse probability weighting

Double robust estimator

Publikations- och innehållstyp

vet (ämneskategori)

ovr (ämneskategori)