{ "70820-17654651": { "datetime_modified":"20191225T143655", "datetime_start":"20200207T100000", "datetime_end":"20200207T120000", "has_end_time":1, "date_start":"2020-02-07", "date_end":"2020-02-07", "time_start":"10:00:00", "time_end":"12:00:00", "time_zone":"America\/Detroit", "event_title":"How Volunteers Can Save Democracy", "occurrence_title":"", "combined_title":"How Volunteers Can Save Democracy: Voters Not Politicians (VPN) Grassroots Change", "event_subtitle":"Voters Not Politicians (VPN) Grassroots Change", "event_type":"Class \/ Instruction", "event_type_id":"8", "description":"Voters Not Politicians (VNP) is the nonpartisan grassroots citizens group that led the 2018 ballot initiative to pass an anti-gerrymandering constitutional amendment. The amendment established an Independent Citizens Redistricting Commission to draw maps for Michigan electoral districts: U.S. Congress and the State House and Senate. VNP is now working to ensure success for the Commission and it is also pursuing other initiatives to enhance democracy. These 3 classes will explain what VNP has accomplished, how VNP created a unique volunteer experience, and how citizens can continue to improve our democracy. Participants will learn how they can participate productively in making Michigan government work better. \r\n\r\nConnie Cook has a Ph.D. in Political Science and recently retired from the University of Michigan. She now serves in a volunteer role as Regional Education Coordinator and Special Counsel to the Executive Director of Voters Not Politicians. Rena Basch has a Ph.D. in Materials Science and Engineering, and fifteen years-experience as an elections administrator, serving as the Ann Arbor Charter Township Clerk. During the VNP campaign for Proposal 2, Rena led the state-wide Outreach Committee, and currently volunteers as the leader of Community Engagement. Connie and Rena will be joined by additional VNP volunteers in sessions on Fridays from February 7 through 21.", "occurrence_notes":null, "guid":"70820-17654651@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/70820", "building_id":"null", "building_name":"Off Campus Location", "campus_maps_id":"1", "room":"", "location_name":"First Presbyterian Church, 1432 Washtenaw Avenue", "has_livestream":0, "cost":"$20", "tags":["Activism","democracy","government","lifelong learning","politics","Redistricting","volunteer"], "website":"http:\/\/olli-umich.org", "sponsors":[ { "group_name":"Osher Lifelong Learning Institute (50+)", "group_id":"3107", "website":"http:\/\/www.olli-umich.org" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2019\/12\/event_70820_original-1.png", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_thumb\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_large_2x\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2019\/12\/event_70820_original-1.png", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb\/media\/attachments\/2019\/12\/event_70820_original-1.png", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb_square\/media\/attachments\/2019\/12\/event_70820_original-1.png", "group_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large\/media\/attachments\/2019\/12\/event_70820_original-1.png", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large_lightbox\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_large_crop\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_list":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_list\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_grid\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_grid_2x\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_large\/media\/attachments\/2019\/12\/event_70820_original-1.png", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_thumb\/media\/attachments\/2019\/12\/event_70820_original-1.png" }, "occurrence_count":1, "first_occurrence":17654651 } , "72393-18000381": { "datetime_modified":"20200203T120440", "datetime_start":"20200207T100000", "datetime_end":"20200207T110000", "has_end_time":1, "date_start":"2020-02-07", "date_end":"2020-02-07", "time_start":"10:00:00", "time_end":"11:00:00", "time_zone":"America\/Detroit", "event_title":"Interdisciplinary Seminar in Quantitative Methods (ISQM)", "occurrence_title":"", "combined_title":"Interdisciplinary Seminar in Quantitative Methods (ISQM): David Blei (Departments of Computer Science and Statistics at Columbia University)", "event_subtitle":"David Blei (Departments of Computer Science and Statistics at Columbia University)", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"The Blessings of Multiple Causes (Joint with Yixin Wang)\r\n\r\nABSTRACT: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference with weaker assumptions than the classical methods require.\r\n\r\nHow does the deconfounder work? While traditional causal methods measure the effect of a single cause on an outcome, many modern scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. The deconfounder uses the correlation among multiple causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference. We demonstrate the deconfounder on real-world data and simulation studies, and describe the theoretical requirements for the deconfounder to provide unbiased causal estimates.\r\n\r\nDavid works in the fields of machine learning and Bayesian statistics.\r\n\r\nThe goal of the Interdisciplinary Seminar in Quantitative Methods is to provide an interdisciplinary environment where researchers can present and discuss cutting-edge research in quantitative methodology. The talks are aimed at a broad audience, with emphasis on conceptual rather than technical issues. The research presented is varied, ranging from new methodological developments to applied empirical papers that use methodology in an innovative way. We welcome speakers and audiences from all disciplines and fields, including the social, natural, biomedical, and behavioral sciences.", "occurrence_notes":null, "guid":"72393-18000381@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/72393", "building_id":"1000167", "building_name":"West Hall", "campus_maps_id":"163", "room":"340", "location_name":"West Hall", "has_livestream":0, "cost":"", "tags":["Political Science"], "website":"", "sponsors":[ { "group_name":"Interdisciplinary Seminar in Quantitative Methods (ISQM)", "group_id":"3559", "website":"" }, { "group_name":"Department of Statistics", "group_id":"3473", "website":"" }, { "group_name":"Department of Political Science", "group_id":"3610", "website":"" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_thumb\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb_square\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "group_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large_lightbox\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_large_crop\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_list":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_list\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_grid_2x\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_large\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_thumb\/media\/attachments\/2020\/02\/event_72393_original-1.jpeg" }, "occurrence_count":1, "first_occurrence":18000381 } , "69917-17483049": { "datetime_modified":"20200130T101909", "datetime_start":"20200207T100000", "datetime_end":"20200207T110000", "has_end_time":1, "date_start":"2020-02-07", "date_end":"2020-02-07", "time_start":"10:00:00", "time_end":"11:00:00", "time_zone":"America\/Detroit", "event_title":"Statistics Department Seminar Series: David Blei, Professor, Department of Statistics and Computer Science, Columbia University", "occurrence_title":"", "combined_title":"Statistics Department Seminar Series: David Blei, Professor, Department of Statistics and Computer Science, Columbia University: \"The Blessings of Multiple Causes [*]\"", "event_subtitle":"\"The Blessings of Multiple Causes [*]\"", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference with weaker assumptions than the classical methods require.\r\n\r\nHow does the deconfounder work? While traditional causal methods measure the effect of a single cause on an outcome, many modern scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. The deconfounder uses the correlation among multiple causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference. We demonstrate the deconfounder on real-world data and simulation studies, and describe the theoretical requirements for the deconfounder to provide unbiased causal estimates.\r\n\r\nThis is joint work with Yixin Wang. [*] https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01621459.2019.1686987\r\n\r\nBiography: David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. He studies probabilistic machine learning, including its theory, algorithms, and application. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship (2017), and a Simons Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research. 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