Causal Inference with Observational Data in R: Propensity Scores, Inverse Probability Treatment Weighting, and Marginal Structural Models
Gary Chan
Abstract
Gary Chan
Abstract
Propensity score methods, Inverse probability treatment weighting, and marginal structural models are relatively new and attractive alternatives to RCTs for causal inference using observational data. This seminar, taught by Dr. Gary Chan, offers an introduction to these methods using R with hands-on instruction and exercises so that you can understand and apply these methods in your own research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Propensity score methods, Inverse probability treatment weighting, and marginal structural models are relatively new and attractive alternatives to RCTs for causal inference using observational data. This seminar, taught by Dr. Gary Chan, offers an introduction to these methods using R with hands-on instruction and exercises so that you can understand and apply these methods in your own research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
Key concepts: Marginal structural model, Causal inference, Inverse probability, Observational study, Propensity score matching, Inverse probability weighting, Weighting, Inference