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        "event_title":"RossAbilities Conference",
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        "description":"Workers with disabilities are among the fastest growing employment demographics in the United States. Business Leaders for Diverse Abilities (BLDA) at the Ross School of Business aims to bring awareness and improve accessibility in the world of business. Join us on February 13th for the 2nd Annual RossAbilities Conference, presented virtually! \n\nRossAbilities is a groundbreaking conference sponsored by the MBA club, BLDA, and is dedicated to exploring the intersection of accessibility and business. Our mission is to bring together leaders, innovators, and entrepreneurs to discuss how accessibility is not just a compliance issue, but a powerful driver of innovation, market growth, and a more inclusive future of work. This year\u2019s keynote speaker is Sara Minkara, U.S. Special Advisor on International Disability Rights during the Biden Administration. We'll also have speakers representing Parasports to talk about emerging trends in sport, learn from entrepreneurs and innovators across accessibility featured applications and businesses, and learn about how Google is leading the way to create more accessible experiences for all! \n\nRegistration is open now on Luma. Here is the link to register: https:\/\/luma.com\/scfihp7h",
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        "tags":["Activism","Business","Career","Entrepreneurship","Graduate School","Inclusion","Leadership","Social Impact","Student Org","Virtual"],
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        "event_title":"Statistics Department Seminar Series: Luke Miratrix, Professor of Education, Faculty Director Doctor of Education Leadership Program, Harvard University",
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        "combined_title":"Statistics Department Seminar Series: Luke Miratrix, Professor of Education, Faculty Director Doctor of Education Leadership Program, Harvard University: \"Caliper Synthetic Matching: Generalized Radius Matching with Local Synthetic Controls\"",
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        "description":"Abstract: Matching promises simple and transparent causal inferences for observational data, making it an attractive approach in many settings, especially given its easily communicated and intuitive rationale. Matching methods \u201cmatch\u201d treated units to control units with similar covariates, with the goal of achieving joint covariate balance between treated and control units, as would be expected in a randomized experiment. In practice, however, standard matching methods often perform poorly compared to more recent approaches such as response-surface modeling and balancing. Finding close matches for treated units becomes particularly challenging when there are many covariates and overlap is low, which can lead to imbalanced matched treatment groups, biased effect estimates, or low effective sample sizes. Building on a host of literature, including synthetic control methods, classic matching approaches, and coarsened exact matching, we propose Caliper Synthetic Matching (CSM) to address challenges with finding quality matches while preserving simple and transparent matching diagnostics. CSM, a version of radial matching, is an adaptive caliper matching method that utilizes locally built synthetic controls to adjust for inexact matches. By combining adaptive calipers and synthetic controls, CSM produces data-driven bounds on potential extrapolation biases while exploiting local linearity to interpolate in a principled manner. Due to the local nature of CSM, we can also detect which units are more difficult to match and assess degree of overlap.  We can even locally adapt caliper width to more tightly control bias in information dense regions. We show that CSM belongs to the monotonic imbalance bounding (MIB) class of matching methods, and that it improves upon the bias bounds for popular MIB methods such as coarsened exact matching. We finally give theoretical results on an inferential strategy that allows for reuse of controls by different treated units (matching with replacement).",
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