Measurement bias and effect restoration in causal inference

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Abstract

This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, it discusses the control of unmeasured confounders in parametric and nonparametric models and the computational problem of obtaining bias-free effect estimates in such models. We derive new conditions under which causal effects can be restored by observing proxy variables of unmeasured confounders with/without external studies.

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