On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives
This work addresses a fundamental problem in causal inference for researchers, showing that certain proposed methods are flawed and offering practical alternatives.
The paper challenges recent claims that unobserved confounding can be overcome in multi-cause causal inference, presenting counterexamples and proving nonparametric identification is impossible, while suggesting alternatives like proxy variables and sensitivity analysis.
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 of work: using proxy variables and shifting focus to sensitivity analysis.