Long Story Short: Omitted Variable Bias in Causal Machine Learning. (arXiv:2112.13398v3 [econ.EM] UPDATED)

Web: http://arxiv.org/abs/2112.13398

We derive general, yet simple, sharp bounds on the size of the omitted
variable bias for a broad class of causal parameters that can be identified as
linear functionals of the conditional expectation function of the outcome. Such
functionals encompass many of the traditional targets of investigation in
causal inference studies, such as, for example, (weighted) average of potential
outcomes, average treatment effects (including subgroup effects, such as the
effect on the treated), (weighted) average derivatives, and policy effects from …

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