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title software abstract layout series publisher issn id month tex_title firstpage lastpage page order cycles bibtex_author author date address container-title volume genre issued pdf extras
Benchmarking Observational Studies with Experimental Data under Right-Censoring
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 and RCT. For the former, we adopt a censoring-doubly-robust signal for the conditional average treatment effect (CATE) to facilitate an equivalence test of CATEs in OS and RCT, which serves as a proxy for testing if the validity assumptions hold. For the latter, we show that the same test can still be used even though unbiased CATE estimation may not be possible. We verify the effectiveness of our censoring-aware tests via semi-synthetic experiments and analyze RCT and OS data from the Women’s Health Initiative study.
inproceedings
Proceedings of Machine Learning Research
PMLR
2640-3498
demirel24a
0
Benchmarking Observational Studies with Experimental Data under Right-Censoring
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4285-4293
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Demirel, Ilker and De Brouwer, Edward and M Hussain, Zeshan and Oberst, Michael and A Philippakis, Anthony and Sontag, David
given family
Ilker
Demirel
given family
Edward
De Brouwer
given family
Zeshan
M Hussain
given family
Michael
Oberst
given family
Anthony
A Philippakis
given family
David
Sontag
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics
238
inproceedings
date-parts
2024
4
18