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SUMMARY:PDHP Workshop: Causal Inference In Observational Studies
DESCRIPTION:Please join for the next installment of the PDHP workshop series: Causal Inference In Observational Studies, presented by Michael Elliott of the University of Michigan Biostatistics & The Program In Survey & Data Science.  This workshop will cover techniques for causal inference using observational data, including theoretical concepts (causal inference via counterfactuals, and how causal inference can be produced from randomized designs), and applied techniques such as estimating causal effects via propensity modelling and matching techniques.  Examples and code using R will also be provided throughout the workshops.\nTopics include:\n\nCausal inference via counterfactuals\nWhy randomized designs yield causal inference\nEstimating causal effects via propensity modelling & matching\nOverlap and its impact on inference\nHands-on practice with example and code using R\n\n\n\n\nAs always, this workshop is free of cost and open to the public, so please feel free to distribute this message to others who may be interested.  For more information and to RSVP, please visit pdhp.isr.umich.edu/workshops.  Light refreshments will be served for those attending in-person.\n\nFor video and resources from our past workshops, please check out the PDHP Youtube playlist and adjoining materials archive.\n\n\n\n
URL:https://psc.isr.umich.edu/events/causal-inference/
CATEGORIES:PDHP Workshop Series
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