Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

arXiv:2404.17019v1 Announce Type: cross
Abstract: A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today's scientists across disciplines. In this paper, we demonstrate that Neyman's methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning algorithms. In particular, we …

abstract arxiv assumptions causal cs.lg evaluation experiment experimental framework machine machine learning paper rules sampling scientists set stat.me stat.ml treatment type