{ "_id": "6abcd01899f7aa8c6aa4c288", "Package": "GenericML", "Title": "Generic Machine Learning Inference", "Version": "0.2.3", "Authors@R": "c(person(given = \"Max\",\nfamily = \"Welz\",\nrole = c(\"aut\", \"cre\"),\nemail = \"max.welz@uzh.ch\",\ncomment = c(ORCID = \"0000-0003-2945-1860\")),\nperson(given = \"Andreas\",\nfamily = \"Alfons\",\nrole = \"aut\",\nemail = \"alfons@ese.eur.nl\",\ncomment = c(ORCID = \"0000-0002-2513-3788\")),\nperson(given = \"Mert\",\nfamily = \"Demirer\",\nrole = \"aut\",\nemail = \"mdemirer@mit.edu\"),\nperson(given = \"Victor\",\nfamily = \"Chernozhukov\",\nrole = \"aut\",\nemail = \"vchern@mit.edu\"))", "Author": "Max Welz [aut, cre] (),\nAndreas Alfons [aut] (),\nMert Demirer [aut], Victor Chernozhukov [aut]", "Maintainer": "Max Welz ", "Description": "Generic Machine Learning Inference on heterogeneous\ntreatment effects in randomized experiments as proposed in\nChernozhukov, Demirer, Duflo and Fernández-Val (2020)\n. This package's workhorse is\nthe 'mlr3' framework of Lang et al. (2019)\n, which enables the specification of a\nwide variety of machine learners. The main functionality,\nGenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo\nand Fernández-Val (2020) for a\nsuite of user-specified machine learners. All steps in the\nalgorithm are customizable via setup functions. Methods for\nprinting and plotting are available for objects returned by\nGenericML(). 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