{"id":763255,"date":"2021-07-26T10:15:56","date_gmt":"2021-07-26T17:15:56","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=763255"},"modified":"2021-07-26T10:15:56","modified_gmt":"2021-07-26T17:15:56","slug":"dowhy-addressing-challenges-in-expressing-and-validating-causal-assumptions","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dowhy-addressing-challenges-in-expressing-and-validating-causal-assumptions\/","title":{"rendered":"DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions"},"content":{"rendered":"

Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed. Violation of any of these assumptions leads to significant error in the effect estimate. However, unlike cross-validation for predictive models, there is no global validator method for a causal estimate. As a result, expressing different causal assumptions formally and validating them (to the extent possible) becomes critical for any analysis. We present DoWhy, a framework that allows explicit declaration of assumptions through a causal graph and provides multiple validation tests to check a subset of these assumptions. Our experience with DoWhy highlights a number of open questions for future research: developing new ways beyond causal graphs to express assumptions, the role of causal discovery in learning relevant parts of the graph, and developing validation tests that can better detect errors, both for average and conditional treatment effects. DoWhy is available at https:\/\/github.com\/microsoft\/dowhy<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed. Violation of any of these assumptions leads to significant error in the effect estimate. However, unlike cross-validation for predictive models, there is no global validator method […]<\/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":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-763255","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-7","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-TR-2021-15","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"ICML 2021 workshop on the Neglected Assumptions in Causal Inference","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":"url","viewUrl":"false","id":"false","title":"https:\/\/drive.google.com\/file\/d\/1i81CnMd683A788RYtEb8KSowhhPJn3Z6\/view","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"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"},{"type":"user_nicename","value":"Vasilis Syrgkanis","user_id":34499,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Vasilis Syrgkanis"},{"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"}],"msr_impact_theme":[],"msr_research_lab":[199565,199561,199562,199563],"msr_event":[],"msr_group":[694878],"msr_project":[587692,596605],"publication":[],"video":[],"download":[],"msr_publication_type":"techreport","related_content":{"projects":[{"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. But what are the outcomes of these interventions, and how can we tune these systems for desired outcomes? In this project we are building methods to estimate the impact of changes to a product feature or a business decision before actually committing to it. 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