{"id":836827,"date":"2022-04-20T01:34:43","date_gmt":"2022-04-20T08:34:43","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=836827"},"modified":"2022-06-02T10:39:52","modified_gmt":"2022-06-02T17:39:52","slug":"community-workshop-on-dowhy-econml","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/community-workshop-on-dowhy-econml\/","title":{"rendered":"Community Workshop on Microsoft’s Causal Tools"},"content":{"rendered":"\n\n\n\n\n

Please join us for a community workshop on Microsoft’s open-source causal learning tools on Tuesday, May 3, 2022, from 9:00-11:00 AM (PT).<\/p>\n\n\n\n

Registration: <\/strong>Please register to attend the workshop (opens in new tab)<\/span><\/a>.<\/s> This event is past, and registration is closed. Videos are available for on-demand viewing.<\/em><\/p>\n\n\n\n

In this workshop, we will hear from people using causal inference in practice about their use cases and experiences; present on Microsoft’s open-source efforts for causality; and host breakout discussions on industry use cases in finance, retail, energy, and others.<\/p>\n\n\n\n

Our goal with Microsoft\u2019s DoWhy+EconML packages is to make answering \u2018what if\u2019 questions a whole lot easier by providing a state-of-the-art, end-to-end framework for causal inference, including the latest causal estimation and automated robustness procedures.<\/p>\n\n\n\n

We look forward to seeing you! In the meantime, please do not hesitate to contact us at caburact@microsoft.com<\/a> if there is anything we can do to be of assistance.<\/p>\n\n\n\n\n\n

Workshop Agenda<\/strong>, Tuesday, May 3, 2022<\/p>\n\n\n\n

<\/p>\n\n\n\n

Please register to attend the workshop (opens in new tab)<\/span><\/a>.<\/s> This event is past, and registration is closed. Videos are available for on-demand viewing.<\/em><\/p>\n\n\n\n

Time (Pacific)<\/th>Session Title<\/th><\/tr><\/thead>
9:00am <\/td>Welcome<\/td><\/tr>
9:05am<\/td>Title: <\/strong>Update on Microsoft causal open-source libraries
Speakers:<\/strong> Eleanor Dillon, Cheng Zhang, Amit Sharma, Chao Ma, Darren Edge
Abstract:<\/strong> In this talk, we will present Microsoft\u2019s open-source causal tools and how they work together.  We\u2019ll review recent updates to DoWhy and EconML and introduce new tools DECI and ShowWhy.  DECI (Deep End-to-end Causal Inference) combines causal discovery and inference; and ShowWhy provides a no-code interface to make causal inference easier for data analysts.<\/td><\/tr>
9:30am<\/td>Fireside chats with domain experts, hosted by Emre K\u0131c\u0131man

10:00am<\/td>Title: <\/strong>Supercharge your A\/B testing with automated causal inference.
Speaker: <\/strong>Egor Kraev, Head of AI, Wise (opens in new tab)<\/span><\/a>
Abstract: <\/strong>An A\/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating the effect conditional<\/em> on customer features (CATE), thus turning customer variability from noise to be averaged over to a valuable source of segmentation, and potentially requiring smaller sample sizes as a result. Unfortunately, there are many different models available for estimating CATE, with many parameters to tune and very different performance. In this talk, we will present our auto-causality library, which combines the three marvelous packages from Microsoft – DoWhy, EconML, and FLAML – to do fully automated selection and tuning of causal models based on out-of-sample performance, just like any other AutoML package does. We will describe the projects inside Wise currently starting to apply it, and present rather striking results on comparative model performance and out-of-sample segmentation on Wise CRM data.  <\/td><\/tr>
10:20am<\/td>Breakout discussions, by industry use cases<\/em><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n","protected":false},"excerpt":{"rendered":"

Please join us for a community workshop on Microsoft’s open-source causal learning tools on Tuesday, May 3, 2022, from 9:00-11:00 AM (PT). Registration: Please register to attend the workshop (opens in new tab). This event is past, and registration is closed. Videos are available for on-demand viewing. In this workshop, we will hear from people […]<\/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":"","msr_startdate":"2022-05-03","msr_enddate":"2022-05-03","msr_location":"Virtual Workshop","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[],"msr-event-type":[210063],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-836827","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-event-type-workshop","msr-locale-en_us"],"msr_about":"\n\n\n\n\n

Please join us for a community workshop on Microsoft's open-source causal learning tools on Tuesday, May 3, 2022, from 9:00-11:00 AM (PT).<\/p>\n\n\n\n

Registration: <\/strong>Please register to attend the workshop<\/a>.<\/s> This event is past, and registration is closed. Videos are available for on-demand viewing.<\/em><\/p>\n\n\n\n

In this workshop, we will hear from people using causal inference in practice about their use cases and experiences; present on Microsoft's open-source efforts for causality; and host breakout discussions on industry use cases in finance, retail, energy, and others.<\/p>\n\n\n\n

Our goal with Microsoft\u2019s DoWhy+EconML packages is to make answering \u2018what if\u2019 questions a whole lot easier by providing a state-of-the-art, end-to-end framework for causal inference, including the latest causal estimation and automated robustness procedures.<\/p>\n\n\n\n

We look forward to seeing you! In the meantime, please do not hesitate to contact us at caburact@microsoft.com<\/a> if there is anything we can do to be of assistance.<\/p>\n\n\n\n\n\n

Workshop Agenda<\/strong>, Tuesday, May 3, 2022<\/p>\n\n\n\n

<\/p>\n\n\n\n

Please register to attend the workshop<\/a>.<\/s> This event is past, and registration is closed. Videos are available for on-demand viewing.<\/em><\/p>\n\n\n\n

Time (Pacific)<\/th>Session Title<\/th><\/tr><\/thead>
9:00am <\/td>Welcome<\/td><\/tr>
9:05am<\/td>Title: <\/strong>Update on Microsoft causal open-source libraries
Speakers:<\/strong> Eleanor Dillon, Cheng Zhang, Amit Sharma, Chao Ma, Darren Edge
Abstract:<\/strong> In this talk, we will present Microsoft\u2019s open-source causal tools and how they work together.  We\u2019ll review recent updates to DoWhy and EconML and introduce new tools DECI and ShowWhy.  DECI (Deep End-to-end Causal Inference) combines causal discovery and inference; and ShowWhy provides a no-code interface to make causal inference easier for data analysts.<\/td><\/tr>
9:30am<\/td>Fireside chats with domain experts, hosted by Emre K\u0131c\u0131man

10:00am<\/td>Title: <\/strong>Supercharge your A\/B testing with automated causal inference.
Speaker: <\/strong>Egor Kraev, Head of AI, Wise<\/a>
Abstract: <\/strong>An A\/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating the effect conditional<\/em> on customer features (CATE), thus turning customer variability from noise to be averaged over to a valuable source of segmentation, and potentially requiring smaller sample sizes as a result. Unfortunately, there are many different models available for estimating CATE, with many parameters to tune and very different performance. In this talk, we will present our auto-causality library, which combines the three marvelous packages from Microsoft - DoWhy, EconML, and FLAML - to do fully automated selection and tuning of causal models based on out-of-sample performance, just like any other AutoML package does. We will describe the projects inside Wise currently starting to apply it, and present rather striking results on comparative model performance and out-of-sample segmentation on Wise CRM data.  <\/td><\/tr>
10:20am<\/td>Breakout discussions, by industry use cases<\/em><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n","tab-content":[],"msr_startdate":"2022-05-03","msr_enddate":"2022-05-03","msr_event_time":"","msr_location":"Virtual Workshop","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"May 3, 2022","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"Please join us for a community workshop on Microsoft's open-source causal learning tools on Tuesday, May 3, 2022, from 9:00-11:00 AM (PT). Registration: Please register to attend the workshop (opens in new tab). This event is past, and registration is closed. Videos are available for on-demand viewing. In this workshop, we will hear from people using causal inference in practice about their use cases and experiences; present on Microsoft's open-source efforts for causality; and host…","msr_research_lab":[],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[470706],"related-projects":[788789,656325,596605],"related-opportunities":[],"related-publications":[],"related-videos":[849445,849187,849454],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/836827","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":22,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/836827\/revisions"}],"predecessor-version":[{"id":849583,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/836827\/revisions\/849583"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=836827"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=836827"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=836827"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=836827"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=836827"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=836827"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=836827"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=836827"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=836827"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}