Скачать с ютуб useR! 2020: Causal inference in R (Lucy D'Agostino McGowan, Malcom Barrett), tutorial в хорошем качестве

useR! 2020: Causal inference in R (Lucy D'Agostino McGowan, Malcom Barrett), tutorial

Lucy D'Agostino McGowan and Malcom Barret give a tutorial on Causal inference in R. The team covers drawing assumptions on a graph, model assumption, analyzing propensities, and estimating causal effect. Throughout the presentation there are exercises provided as well as a walk through afterwards. This video is part of the virtual useR! 2020 conference. Find supplementary material on our website https://user2020.r-project.org/. Main Sections 0:00 Introduction 2:53 Three best practices of analysis 12:03 Causal modeling in R: whole game 24:30 Diagnose your model assumptions 27:18 Estimate the causal effects 30:05 Using {rsample} to bootstrap our causal effect 33:15 Review the R markdown file later! 33:55 Resources 35:15 Causal diagrams in R 37:39 The basic idea 39:17 ggdag 44:09 Exercise 1 50:33 Causal effects and backdoor paths 53:10 Exercise 2 1:01:19 Exercise 3 1:07:19 Resources: ggdag vignettes 1:08:43 Propensity Scores 1:15:52 Exercise 1 1:20:10 Walk through 1:25:28 Propensity scores weighting 1:36:55 Exercise 2 1:40:35 Walkthrough 1:42:32 Propensity score diagnostic 1:44:43 SMD in R 1:53:43 Outcome model 1:57:09 Exercise 2:08:58 Walkthrough 2:11:30 Thank you! More Resources Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Blog: https://www.r-consortium.org/news/blog Join: https://www.r-consortium.org/about/join Twitter:   / rconsortium   LinkedIn:   / r-consortium