Doubly Robust Estimation of Causal Effects in R by Dr. Sebastian Teran Hidalgo. Visit https://rstats.ai/nyr/ to learn more. Abstract: This talk will be a crash course on how to estimate causal treatment effects. Usually in a randomized experiment or A/B test it is easy to estimate the average causal effect of intervention A compared to B with a simple difference in means. However, in many situations randomized data is not available but a data scientist might still try to estimate the causal effect of A compared to B. Doubly robust estimators can estimate this causal effect, under certain assumptions, and allow the data scientists two chances to get the correct answer. This estimator will be built from the ground up using R. Bio: Sebastian Teran Hidalgo is a data scientist at Vroom, an e-commerce start-up that focuses on selling used cars that are delivered to your door. Previously, Sebastian was doing research on cancer genomics as a postdoc at Yale University. He holds a PhD in Biostatistics from UNC-Chapel Hill. Twitter: https://twitter.com/steranhidalgo Presented at the 2020 R Conference | New York (August 14th, 2020)