1.简介You’ve found the online causal inference course page. Although, the course text is written from a machine learning perspective, this course is meant to be for anyone with the necessary prereq…
Sort options Sort byStart Date AscStart Date DescUpdated Date AscUpdated Date DescTitle AscTitle Desc Course Title Contains Initiative/Provider University/Entity Categories Subjects/Skills Course Length Start Date A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera) ...
You’ve found the online causal inference course page. Although, the course text is written from a machine learning perspective, this course is meant to be for anyone with the necessaryprerequisiteswho is interested in learning the basics of causality. I do my best to integrate insights from th...
Kohavi, Tang, and Xu, Trustworthy Online Controlled Experiments Imbens and Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences Angrist and Pischke, Mostly Harmless Econometrics Course Plan Lecture 1: Introduction; case studies; importance of causality; importance of handling high dimen...
(Global, weekly reading group) Online Causal Inference Seminar. Organized by Stanford, ETH, etc. [speakers] [past recordings] Every Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin). (Current) Causal Seminar@Harvard Data Science Institute. [talks] (2021, ...
人工智能可能更偏向于使用机器学习或深度学习的工具实现高维度数据的Causal learning，传统Causal inference...
Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978;6:34-58. doi:10.1214/aos/1176344064 Google ScholarCrossref 5. Rubin DB. Randomization analysis of experimental data: the Fisher randomization test comment. J Am Stat Assoc. 1980;75(371):591-593...
The SUTVA, Positivity, Identifiability, Consistency, Exchangeability of Causal Inference, the essential ingredients that helps us bring out the true flavor of the causal model. Here is my understanding of each assumptions (main course) with examples (sid
In the first part, total, direct, and indirect effects are defined, the second part deals with causal inference, i.e., in the second part it is shown how causal effects are identified by estimable quantities. In each part, there are two levels, a disaggregated and a reaggregated one. ...
causal-inferencecausal-modelsuplift-modelingcausal-forestbayesian-additive-regression-treesprofit-maximization UpdatedSep 30, 2020 R jiachenghe666/Project904 Star1 Code Issues Pull requests A course project for POLS 904 Statistical Computing Foundations ...