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Excited that Maastricht University and Copenhagen Business School is hosting a 2-day conference in causal inference this week. Looking forward to great discussions on inference techniques with high dimensional data 🚀

Belated welcome to the Causal Data Science Meeting 2020. Next year you will be able to drop the "Causal" from the title; "data science" will mean "science", not "data".

At my @causal_science keynote yesterday, someone asked for pointers to work on causal modeling with spatial data. I thought this paper was a very good overview: deepai.org/publication/a-…

Tonight I had a lot of fun talking with Martin Hétu Beyers Louw and Leo Schmallenbach about their great research AOM TIM Division Virtual Research Workshop. All are PhD students in top European universities and work on fantastic ideas on technology and innovation. Follow them!

It is better to get a probabilistic answer to the right question, e.g., "was this accident caused by a reckless driver?", than a certain answer to the wrong question, e.g., "does reckless driving cause accidents?". That's the science of "Causes of Effects" ucla.in/2L9JHNu

Success! My package📦causaldata 📦 is available on CRAN (install.packages('causaldata')), ssc (ssc install causaldata), and PyPI (pip install causaldata). causaldata contains data sets used in The Effect (by me), The Mixtape (scott cunningham), and What If (Miguel Hernán and Robins)

Great paper. Feel like the dangers of bad controls are still poorly understood outside Pearls framework. Not uncommon to hear CI practitioners say things like: let’s just stick everything in X, just in case it’s a confounder.

SI Assistant Prof Paul Hünermund (and Jermain Kaminski) is arranging The Causal Data Science Meeting 2021 (Nov 15–16, 2021). Submit a paper, join if you are interested in causal inference methods. bit.ly/3BsJv5N

Join for the Causal Data Science Meeting on 15-16 November! A meeting point on causality between between academia and industry ♾ @causal_science #cdsm

Crucial fundamentals "(1) the ability to generate a counterfactual from a model of reality and (2) understand the relationship between your model of reality and what you expect the data to do for you" - Judea Pearl Panel at the MSFT Research Summit '21

We are excited to release the Python causal-learn package for causal discovery! See the package (github.com/cmu-phil/causa…) and documentation (causal-learn.readthedocs.io/en/latest/). Any feedback is welcome.

Join us today and tomorrow Cesar David

Excited to start this year’s #CDSM21 with my colleagues @PHuenermund , Carla Schmitt, Beyers Louw and all speakers. Two days dedicated to causal inference in data science ✌🏼.

Excited to host Prof Judea Pearl at the Causal Data Science Meeting this year. Check out the rest of the program: causalscience.org #CDSM22

The Call for Papers for the Causal Data Science Meeting 2023 is out. We're very happy that we can already announce our first keynote: Dominik Janzing from Amazon Science. 🗓️Mark your calendars for Nov 7–8. #CDSM23. More info & pre-registration (free): causalscience.org

The program of the Causal Data Science Meeting 2023 (Nov 7–8) is now available on our website. Check out the fantastic set of presentations and register for free at causalscience.org. Co-organized with @PHuenermund, Carla Schmitt & Beyers Louw.