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Automated causal inference in application to randomized controlled clinical trials. (arXiv:2201.05773v2 [stat.ME] UPDATED)
Jan. 20, 2022, 2:11 a.m. | Jiqing Wu, Nanda Horeweg, Marco de Bruyn, Remi A. Nout, Ina M. Jürgenliemk-Schulz, Ludy C.H.W. Lutgens, Jan J. Jobsen, Elzbieta M. van der Steen-
cs.LG updates on arXiv.org arxiv.org
Randomized controlled trials (RCTs) are considered as the gold standard for
testing causal hypotheses in the clinical domain. However, the investigation of
prognostic variables of patient outcome in a hypothesized cause-effect route is
not feasible using standard statistical methods. Here, we propose a new
automated causal inference method (AutoCI) built upon the invariant causal
prediction (ICP) framework for the causal re-interpretation of clinical trial
data. Compared to existing methods, we show that the proposed AutoCI allows to
efficiently determine the …
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