{"id":900081,"date":"2022-11-21T20:50:24","date_gmt":"2022-11-22T04:50:24","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2023-08-17T08:37:41","modified_gmt":"2023-08-17T15:37:41","slug":"a-causal-ai-suite-for-decision-making","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-causal-ai-suite-for-decision-making\/","title":{"rendered":"A Causal AI Suite for Decision-Making"},"content":{"rendered":"
Critical data science and decision-making questions across a wide variety of domains are fundamentally causal questions. We present a suite of open-source causal tools and libraries that aims to simultaneously provide core causal AI functionality to practitioners and create a platform for research advances to be rapidly deployed. In this paper, we describe our contributions towards such a comprehensive causal AI suite of tools and libraries, its design, and lessons we are learning from its growing adoption. We hope that our work accelerates use-inspired basic research for improvement of causal AI.<\/p>\n","protected":false},"excerpt":{"rendered":"
Critical data science and decision-making questions across a wide variety of domains are fundamentally causal questions. We present a suite of open-source causal tools and libraries that aims to simultaneously provide core causal AI functionality to practitioners and create a platform for research advances to be rapidly deployed. In this paper, we describe our contributions […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[265008,248617,248659],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-900081","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-field-of-study-causal-discovery","msr-field-of-study-causal-inference","msr-field-of-study-causal-model"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2022-12-2","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/CausalAISuiteForDecisionMaking.pdf","id":"900084","title":"causalaisuitefordecisionmaking","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":900084,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/11\/CausalAISuiteForDecisionMaking.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Emre Kiciman","user_id":31739,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Emre Kiciman"},{"type":"user_nicename","value":"Eleanor Dillon","user_id":38467,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Eleanor Dillon"},{"type":"user_nicename","value":"Darren Edge","user_id":31509,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Darren Edge"},{"type":"guest","value":"adam-foster","user_id":797251,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=adam-foster"},{"type":"guest","value":"agrin-hilmkil","user_id":841858,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=agrin-hilmkil"},{"type":"guest","value":"joel-jennings","user_id":841852,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=joel-jennings"},{"type":"user_nicename","value":"Chao Ma","user_id":42870,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Chao Ma"},{"type":"user_nicename","value":"Robert Osazuwa Ness","user_id":41009,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Robert Osazuwa Ness"},{"type":"user_nicename","value":"Nick Pawlowski","user_id":40997,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nick Pawlowski"},{"type":"user_nicename","value":"Amit Sharma","user_id":30997,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Amit Sharma"},{"type":"user_nicename","value":"Cheng Zhang","user_id":37428,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Cheng Zhang"}],"msr_impact_theme":[],"msr_research_lab":[199561,199562,199563,199565],"msr_event":[],"msr_group":[470706,901101],"msr_project":[656325,587692,596605],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":656325,"post_title":"EconML","post_name":"econml","post_type":"msr-project","post_date":"2020-06-02 09:40:48","post_modified":"2022-02-24 07:11:27","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/econml\/","post_excerpt":"EconML\u00a0is an open source Python package developed by the ALICE team at Microsoft Research that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data. The suite of estimation methods provided in EconML represents the latest advances in causal machine learning. By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/656325"}]}},{"ID":587692,"post_title":"Project Causica: Decision Optimization with Causal ML","post_name":"project_azua","post_type":"msr-project","post_date":"2020-02-26 05:01:04","post_modified":"2024-02-28 03:27:19","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project_azua\/","post_excerpt":"Project Causica aims to develop machine learning solutions for efficient decision making that demonstrate human expert-level performance across all domains.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/587692"}]}},{"ID":596605,"post_title":"DoWhy: Causal Reasoning for Designing and Evaluating Interventions","post_name":"dowhy","post_type":"msr-project","post_date":"2019-07-07 23:24:58","post_modified":"2021-12-08 02:18:56","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/dowhy\/","post_excerpt":"Today's computing systems can be thought of as interventions in people's work and daily lives. 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