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Elias Bareinboim Profile picture
@eliasbareinboim
Dec 7, 2021 • 6 tweets • 7 min read • Read on X
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1/6 If you are interested in causal inference & machine learning, I am excited to share some of the latest work of the causal artificial intelligence group that will appear @NeurIPSConf this year. We welcome you to stop by and chat with us at the following times… #NeurIPS2021
2/6 Tue 11:30 am (EST) “The Causal-Neural Connection: Expressiveness, Learnability, and Inference”, with Kevin Xia, Kai-Zhan Lee, and Yoshua Bengio. Link: causalai.net/r80.pdf.
3/6 Tue 11:30 am (EST) + Oral Fri 7:40 pm (EST) “Sequential Causal Imitation Learning with Unobserved Confounders”, with @danielkumor & Junzhe Zhang. Link: causalai.net/r76.pdf.
4/6 Tue 11:30 am (EST) (spotlight) “Double Machine Learning Density Estimation for Local Treatment Effects with Instruments”, with @YonghanJung & Jin Tian. Link: causalai.net/r75.pdf.
5/6 Thu 3:30 am (EST) + Oral Tue 3:40 am (EST) “ Causal Identification with Matrix Equations”, with @sanghack. Link: causalai.net/r70.pdf.
6/6 Wed 7:30 pm (EST) “Nested Counterfactual Identification from Arbitrary Surrogate Experiments”, with Juan Correa & @sanghack. Link: causalai.net/r79.pdf.

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More from @eliasbareinboim

Elias Bareinboim Profile picture
Jan 30, 2021
1/4 Broadly, because estimating a cond. prob. P(Y | X) is easier than a joint one, P(Y, X, Z), when X & Z are high-dimensional. Indeed, this observation led to (non-causal) graphical models in the 1980s, including Bayesian nets (ie, non-causal DAGs), Markov random fields & so on.
2/4 The goal in this journey was to find parsimonious encoding of distributions that may lead to efficient learning-inference (e.g., belief propagation). There is the whole UAI community, which is now permeated throughout other ML conferences, focused on this class of algorithms.
3/4 This was insufficient for causal reasoning, as noted in the early 1990's & discussed in Sec. 1.4 here causalai.net/r60.pdf (e.g., see ex. 10). It has been a quite traumatic transition, as noted by @yudapearl , see quote found in p. 30:
Read 4 tweets
Elias Bareinboim Profile picture
Jul 8, 2019
1/5 The Backdoors, Frontdoors, IVs & Duplexes of the world are just specific identification (ID) strategies that should come later, in other words, the premise of the discussion seems ‘odd’ to me. First, one should model the phenomenon under investigation using its best science,
2/5 regardless of what her/his favorite ID strategies are. After having a model of reality (e.g., a causal DAG), she/he can go on and discuss what ID strategy is more suitable for answering the specific query. As I said, I find it a bit curious that CS folks &
3/5 methodologists are clearly against starting w/ any procedure (e.g., backdoor, g-computation), while some empirical scientists seem to be thinking first about these procedures, conflating ID strategy & model construction. In summary: model reality first, ID & estimation later.
Read 5 tweets
Elias Bareinboim Profile picture
Jun 9, 2019
1/n Dear friends, I am pleased to announce that after four productive years here at Purdue, I decided to move to Columbia University, starting on this July 1st. I am excited about the new possibilities and adventures that we’ll have in causality-land in the next years, both in
2/n terms of the science as well as the applications of causal inference (CI).

I want to share a Q I have been asked more frequently in the last years, which is whether I believe CI is done, at least in terms of basic principles, and if all that is needed are applications.
3/n I couldn't disagree more with the statement. (For details, check my talk Judea alluded, which is available @ cs.columbia.edu/streaming/2019…).

I don't think anyone would deem molecular biology "solved", and ready to apply (e.g., to solve cancer) when Watson and Crick published...
Read 8 tweets

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