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https://statweb.stanford.edu/~owen/courses/363/stats361.pdf
https://statweb.stanford.edu/~owen/courses/363/

https://arxiv.org/abs/2012.09092

Some books are presented as cookbook style, a new section for them.

http://proceedings.mlr.press/v67/gutierrez17a.html

https://www.cambridge.org/core/journals/econometric-theory/article/abs/trygve-haavelmo-and-the-emergence-of-causal-calculus/C23F960A9518C7C2F14B97BD2901E4D2

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.168702

• https://arxiv.org/pdf/1305.5506.pdf
• https://ftp.cs.ucla.edu/pub/stat_ser/r402.pdf

add more

https://jmlr.org/papers/volume10/white09a/white09a.pdf

Baseless claim "... the fact that the PCM is not as well suited to analyzing structures that exhibit features of central interest to economists and econometricians.."
They omit the Elephant in the room that the first causal inference approach taken by Haavelmo - Economics Nobel

https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00295

https://www.biorxiv.org/content/10.1101/253351v5.full

• recent updates to DoWhy and EconML and introduce new tools DECI and ShowWhy. DECI (Deep End-to-end Causal Inference) combines causal discovery and inference; and ShowWhy provides a no-code interface to make causal inference easier for data analysts.
• three marvelous packages from Microsoft – DoWhy, EconML, and FLAML

start who's who section: scientists/groups working on causality related fields.

New section on RL.

software: https://pair-code.github.io/what-if-tool/index.html

Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Donald B Rubin
Journal of the American Statistical Association; Mar 2005; 100, 469; ABI/INFORM Global

• Causality: Models, Reasoning and Inference (2009) 2nd Edition.
  amzn.

http://papers.nips.cc/paper/6995-counterfactual-fairness

Achieving Causal Fairness in Machine Learning
Wu, Yongkai. University of Arkansas, ProQuest Dissertations Publishing, 2020. 27960037.

https://search.proquest.com/openview/5b94283bd4da8edc1b14bff3db4c9e77/1?pq-origsite=gscholar&cbl=18750&diss=y

• Imbens2020 here arXiv | reply-by-JP

Improving the accuracy of medical diagnosis
with causal machine learning
http://web.cs.ucla.edu/~kaoru/richens-etal2020.pdf

Nino Malekovic, The Hague Centre for Strategic Studies.

• The first two references translate Pearl's causal graphs to time-series data: https://advances.sciencemag.org/content/5/11/eaau4996
  https://aip.scitation.org/doi/full/10.1063/1.5025050

• This is an unofficial literature review of causality in time-series data: https://towardsdatascience.com/inferring-causality-in-time-series-data-b8b75fe52c46

• You might find interesting that Pearl suggested the Sure-Thing Principle as an axiomatic basis for Bayesian inference: https://ftp.cs.ucla.edu/pub/stat_ser/r466.pdf

• Finally, here's a quantum-like interpretation of the violations of the Sure-Thing principle: https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00011/full

CATE papers; main ones.

CV : https://usaito.github.io/files/ICML2020_CFCV.pdf

https://www.researchgate.net/publication/228774938_Causal_Complexity_and_the_Study_of_Politics

http://web.cs.ucla.edu/~kaoru/primer-ch4.pdf

here & relevant ones. New section.

critique

Add survey

https://arxiv.org/abs/1912.09104
Haavelmo's original

https://www.ems-ph.org/JOURNALS/show_abstract.php?issn=1660-8933&vol=16&iss=2&rank=11

https://link.springer.com/content/pdf/10.1007/s10827-010-0262-3.pdf

https://arxiv.org/pdf/2111.12161.pdf

https://deepmind.com/blog/article/Causal_Bayesian_Networks