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Fresh Example of Simpson’s Paradox in Clinical Trials

A fresh example of Simpson’s Paradox in the wild has recently emerged! Here’s the story: some researchers and policymakers argue that the NIH (National Institutes of Health) funds too many “small and crappy” trials, which has been regularly reported in the press.

Review of Chernozhukov’s Causal ML Textbook

On Twitter/X, there’s been a lot of discussion about Chernozhukov’s textbook, which came out on February 24th. Having read several chapters in detail and skimmed through the rest, here’s my take on it.

Alternative Approach to Causal Libraries

Having recently done a review of libraries on causality, I have the impression that Microsoft’s option is the most viable among others, since it has technical support, promotion and community support. However, I am not mostly a big fan of this “specify all the parameters you can and step aside” approach.

Trends from the Third Annual Causal Data Science Meeting

Not so long ago, the third annual Causal Data Science Meeting was held on November 7-8. Among the trends this year, it is worth noting that, unlike in previous years, there are more and more papers on methods directly working in or being developed for production environments.

List of Causal Libraries

Name	Team	Software	Keywords	link
causalai	Salesforce	Python	Discovery \| Time Series \| basic-inference	https://github.com/salesforce/causalai
pycid	Causal Incentives Working Group a collaboration with Oxford, DeepMind and Toronto researchers	Python	bayesian-networks \| influence-diagrams	https://github.com/causalincentives/pycid
causal-learn	Center for Causal Discovery, pittsbugh	Python	discovery \|hidden-causal \| Time Series basic-inference \| tetrad	https://github.com/causalincentives/pycid
CausalImpact	Google, Inc	R	Bayesian time-series	https://github.com/google/CausalImpact
EconML	Microsoft Research	Python	Machine Learning Based Estimation of Heterogeneous Treatment Effects	https://econml.azurewebsites.net/index.html
causalnex	Petuum	Python	augment model \| structure learning	https://causalnex.readthedocs.io/en/latest/index.html
dowhy	Microsoft Research	Python	Double ML \| Matching \| Balancing Synthetic control Outcome-based Learners (e.f. X-Learner) Dif&Dif \| basic-inference	https://github.com/py-why/dowhy
causalToolbox	University of California	R	X-learner	https://github.com/forestry-labs/causalToolbox
showwhy	Microsoft Research	TypeScript	no-code interfaces	https://github.com/microsoft/showwhy
causalimages-software	The University of Texas at Austin	R	computer-vision \| earth-observation biomedical-image-analysis	https://github.com/cjerzak/causalimages-software
gCastle	Huawei Noah's Ark Lab	Python	Structure Learning gradient-based learning	https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
causallib	BiomedSciAI	Python	basic-inference	https://github.com/BiomedSciAI/causallib
causal-cmd	Center for Causal Discovery, pittsbugh	java	CMD based \| discovery	https://bd2kccd.github.io/docs/causal-cmd/
ResearchMap	UCLA	JS	Optimal experiments \| augmented models	https://github.com/ResearchMaps/

Список для чтения по поведенческой и экспериментальной экономике

5 лет назад совместно с «Всероссийской государственной библиотекой иностранной литературы имени М.И.Рудомино» я сделал рекомендательный список литературы и периодики, посвященных поведенческой экономике. Публикую его сегодня, следуя рекомендации, когда-то данной мне Быковским – спустя 5 лет, и сопровождая сегодняшними комментариями.