On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives
Abstract
Unobserved confounding is a central barrier to drawing causal inferences from observational data.
Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously.
In this paper, we present two simple, analytical counterexamples that challenge the general claims that are central to these approaches.
In addition, we show that nonparametric identification is impossible in this setting.
We discuss practical implications, and suggest alternatives to the methods that have been proposed so far in this line or work: using proxy variables and shifting focus to sensitivity analysis.
Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously.
In this paper, we present two simple, analytical counterexamples that challenge the general claims that are central to these approaches.
In addition, we show that nonparametric identification is impossible in this setting.
We discuss practical implications, and suggest alternatives to the methods that have been proposed so far in this line or work: using proxy variables and shifting focus to sensitivity analysis.
Research Areas
