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Matching Methods for Causal Inference from Duke’s Almost-Matching-Exactly Lab | Dr. Cynthia Rudin

Presentation by Dr. Cynthia Rudin, Duke University | This presentation highlights work by Duke University's Almost-Matching-Exactly Lab to develop #matchingmethods for #causalinference using statistical #machinelearning algorithms. The talk describes an #energy challenge that initially motivated this research (prevention of fires and explosions involving manholes in Manhattan) and explains how the Fast, Large, Almost Matching Exactly (FLAME) algorithm works. In general, the lab's #algorithms match units with similar covariate distributions, creating high quality, exact or almost exact matches for treatment effect estimation--with applications for energy, healthcare, and many other sectors. Explore the lab's software, including the DAME-FLAME #Python Package, FLAME #R Package, MALTS Python Package, and AHB R Package: https://almost-matching-exactly.githu... 0:00 - 9:21 - Presentation 9:21 - end - Audience Q&A About the Presenter: Dr. Cynthia Rudin is a Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning, including applications to energy systems. She is also an Associate Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Dr. Rudin has a Ph.D. in Applied and Computational Mathematics from Princeton University. This presentation was a part of the Energy Data Analytics Symposium hosted by Duke University on December 8-9, 2020. The Symposium’s theme was “Transforming Energy Systems with Data Science Techniques.” Learn more about the 2020 Energy Data Analytics Symposium and view all presentations: https://energy.duke.edu/energy-data-a... All presentations are also available on a YouTube playlist:    • Energy Data Analytics Symposium   The 2020 Energy Data Analytics Symposium was organized by the Energy Data Analytics Lab at Duke University and was supported by a grant from the Alfred P. Sloan Foundation. Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff. Learn about the Energy Data Analytics Lab at Duke University: https://energy.duke.edu/research/ener... Get email updates on energy news and events at Duke University: https://bit.ly/energyduke