Causal Inference With Selectively-Deconfounded Data

25 Feb 2020 • Kyra Gan • Andrew A. Li • Zachary C. Lipton • Sridhar Tayur

Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner must then either (a) collect deconfounded data; (b) run a clinical trial; or (c) elucidate further properties of the causal graph that might render the ATE identifiable... (read more)

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