awesome-casual-inference 
resource of casual inference
Table of Content
1.Book
因果革命 | The Book of Why - Judea Pearl ; 2019.
Causal Inference for Statistics, Social, and Biomedical. Sciences: An Introduction. Guido W. Imbens and. Donald B. Rubin.
2.Course/Seminar/Video
2.1 Course
| Tutorial Title | Source | Year | Materials |
|---|---|---|---|
| A Crash Course in Causality: Inferring Causal Effects from Observational Data | Coursera | 2017~ | [Tutorial] |
| Introduction to Causal Inference | Brady Neal | 2020 Fall | [Tutorial] |
2.2 Seminar
| Tutorial Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| Tutorial on Causal Inference and Counterfactual Reasoning | KDD | 2018 | Microsoft ; Amit Sharma (@amt_shrma), Emre Kiciman (@emrek) | [Gitbook] |
3.Expert
Judea Pearl [Website] - Prof. @UCLA. Probabilistic and causal reasoning, create the causaul diagrams to research casual effect.
Peng Ding[Website] - Prof. @UC Berkley. Causal Inference.
Brady Neal [Website] - Phd student in causal inference and machine learning at Mila - Quebec AI Institute , co-advised by Ioannis Mitliagkas and Yoshua Bengio. (2020)
4.Library
Mainly listed python package.
dowhy : An end to end library for casual inference. I think this could be a little not flexible to deal with the specific data format and need to spend more time to research it. @Microsoft
causalml : A Python Package for Uplift Modeling and Causal Inference with ML. @Uber
statsmodels : statsmodels.tsa.statespace contains classes and functions that are useful for time series analysis using state space methods.
awesome-causality-algorithms : updated papers/datasets in this repo.
5.Paper
| Paper Title | Author (Co-first) | Year | Materials |
|---|---|---|---|
| Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasonin | Judea Pearl | 1985 | [PDF] |
| A graph based logic for reasoning about relevance relations | Judea Pearl | 1985 | [PDF] |
| A Survey on Causal Inference | Liuyi Yao | 2020 | [PDF] |
| Causal inference in statistics: An overview | Judea Pearl | 2005 | [PDF] |
| What are the most important statistical ideas of the past 50 years? | Andrew Gelman | 2021 | [PDF] |
Reference
Statistics
醫學統計學筆記 [GitBook] - 統計筆記, bayesian statistics (94-103), causal inference (106-112)
看見統計 [Website] - 統計推斷, 貝葉斯學派.
因果推斷學習筆記 [Blog] - Yishi Lin.
Capital of Statistics [Blog] - 因果推斷簡介系列文