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Benchmarking Observational Studies with Experimental Data under Right-Censoring
Feb. 26, 2024, 5:45 a.m. | Ilker Demirel, Edward De Brouwer, Zeshan Hussain, Michael Oberst, Anthony Philippakis, David Sontag
stat.ML updates on arXiv.org arxiv.org
Abstract: Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major limitation of existing procedures is not accounting for censoring, despite the abundance of RCTs and OSes that report right-censored time-to-event outcomes. We consider two cases where censoring time (1) is independent of time-to-event and (2) depends on time-to-event the same way in OS …
abstract accounting arxiv assumptions benchmarking data experimental inferences major stat.me stat.ml studies type
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