The Proximal ID Algorithm
This work addresses a fundamental challenge in causal inference for researchers and practitioners, offering a more general identification algorithm that can handle unobserved confounders, though it appears incremental as it builds on existing approaches.
The paper tackles the problem of unobserved confounding in causal inference by developing the proximal ID algorithm, which synthesizes graphical and proxy-based approaches to achieve nonparametric identification in multivariate systems, including cases where previous methods fail.
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to address this obstacle: obtaining identification using fortuitous external aids, such as instrumental variables or proxies, or by means of the ID algorithm, using Markov restrictions on the full data distribution encoded in graphical causal models. In this paper we aim to develop a synthesis of the former and latter approaches to identification in causal inference to yield the most general identification algorithm in multivariate systems currently known -- the proximal ID algorithm. In addition to being able to obtain nonparametric identification in all cases where the ID algorithm succeeds, our approach allows us to systematically exploit proxies to adjust for the presence of unobserved confounders that would have otherwise prevented identification. In addition, we outline a class of estimation strategies for causal parameters identified by our method in an important special case. We illustrate our approach by simulation studies and a data application.