High Dimensional Causal Inference with Variational Backdoor Adjustment

Daniel Mingyi Israel, Aditya Grover, Guy Van den Broeck

Keywords: causality, causal inference, backdoor adjustment, variational inference, generative model, high dimensional, intervention, treatment, confounding, identifiable, optimization

TL;DR: Applying variational inference to compute backdoor adjustment in the context of high dimensional direct confounding

Abstract: Backdoor adjustment is a technique in causal inference for estimating interventional quantities from purely observational data. In medical settings, backdoor adjustment can be used to control for confounding and isolate the effectiveness of a treatment. However, high dimensional treatments and confounders pose a series of potential pitfalls: tractability, identifiability, optimization. In this work, we take a generative modeling approach to backdoor adjustment for high dimensional treatments and confounders. We cast backdoor adjustment as an optimization problem in variational inference without reliance on proxy variables and hidden confounders. Empirically, our method is able to estimate interventional likelihood in a variety of high dimensional settings, including semi-synthetic X-ray medical data. To the best of our knowledge, this is the first application of backdoor adjustment in which all the relevant variables are high dimensional.

Primary Area: causal reasoning

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Submission Number: 7766