{"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/benchmarking-framework-for-performance","title":"Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis","arxiv_id":"1802.05046","date":"2018-02-14","proceeding":null,"authors":["Yishai Shimoni","Chen Yanover","Ehud Karavani","Yaara Goldschmnidt"],"abstract":"Causal inference analysis is the estimation of the effects of actions on\noutcomes. In the context of healthcare data this means estimating the outcome\nof counter-factual treatments (i.e. including treatments that were not\nobserved) on a patient's outcome. Compared to classic machine learning methods,\nevaluation and validation of causal inference analysis is more challenging\nbecause ground truth data of counter-factual outcome can never be obtained in\nany real-world scenario. Here, we present a comprehensive framework for\nbenchmarking algorithms that estimate causal effect. The framework includes\nunlabeled data for prediction, labeled data for validation, and code for\nautomatic evaluation of algorithm predictions using both established and novel\nmetrics. The data is based on real-world covariates, and the treatment\nassignments and outcomes are based on simulations, which provides the basis for\nvalidation. In this framework we address two questions: one of scaling, and the\nother of data-censoring. The framework is available as open source code at\nhttps://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework","url_abs":"http://arxiv.org/abs/1802.05046v2","url_pdf":"http://arxiv.org/pdf/1802.05046v2.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":"benchmarking-framework-for-performance","repo_url":"https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"benchmarking-framework-for-performance","repo_url":"https://github.com/IBM-HRL-MLHLS/IBM-Causality-Benchmarking-Framework","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.05046","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}