{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-in-policy-evaluation-new","title":"Machine learning in policy evaluation: new tools for causal inference","arxiv_id":"1903.00402","date":"2019-03-01","proceeding":null,"authors":["Noemi Kreif","Karla DiazOrdaz"],"abstract":"While machine learning (ML) methods have received a lot of attention in\nrecent years, these methods are primarily for prediction. Empirical researchers\nconducting policy evaluations are, on the other hand, pre-occupied with causal\nproblems, trying to answer counterfactual questions: what would have happened\nin the absence of a policy? Because these counterfactuals can never be directly\nobserved (described as the \"fundamental problem of causal inference\")\nprediction tools from the ML literature cannot be readily used for causal\ninference. In the last decade, major innovations have taken place incorporating\nsupervised ML tools into estimators for causal parameters such as the average\ntreatment effect (ATE). This holds the promise of attenuating model\nmisspecification issues, and increasing of transparency in model selection. One\nparticularly mature strand of the literature include approaches that\nincorporate supervised ML approaches in the estimation of the ATE of a binary\ntreatment, under the \\textit{unconfoundedness} and positivity assumptions (also\nknown as exchangeability and overlap assumptions).\n  This article reviews popular supervised machine learning algorithms,\nincluding the Super Learner. Then, some specific uses of machine learning for\ntreatment effect estimation are introduced and illustrated, namely (1) to\ncreate balance among treated and control groups, (2) to estimate so-called\nnuisance models (e.g. the propensity score, or conditional expectations of the\noutcome) in semi-parametric estimators that target causal parameters (e.g.\ntargeted maximum likelihood estimation or the double ML estimator), and (3) the\nuse of machine learning for variable selection in situations with a high number\nof covariates.","url_abs":"http://arxiv.org/abs/1903.00402v1","url_pdf":"http://arxiv.org/pdf/1903.00402v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-in-policy-evaluation-new","repo_url":"https://github.com/KDiazOrdaz/Machine-learning-in-policy-evaluation-new-tools-for-causal-inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"variable-selection","task_name":"Variable Selection"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[{"method_slug":"counterfactuals","method_name":"Counterfactuals"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.00402","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}