Article

Causal Forests versus Inverse Probability of Treatment Weighting to adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study based on US Hosptial Electronic Health Record Data

2025-04-29

Abstract excerpt

<h4>Background</h4> Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confounding related to patient-level and surgeon or hospital covariates hampers observational studies of risks and benefits. We conducted two simulation studies to compare the performance of Causal Forests (CF) vs Inverse Probability of Treatment Weighting...

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Literature Corpus work: 38bb204b-340c-55ee-8d54-b4efbc8d6d4b
DOI: 10.1101/2025.04.29.25326430

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Causal Forests versus Inverse Probability of Treatment Weighting to adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study based on US Hosptial Electronic Health Record DataDOI 10.1101/2025.04.29.25326430Read

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