Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, Jose Zubizarreta
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
https://arxiv.org/abs/2508.17099

Are you interested in improving the #interpretability, #robustness and #safety of current AI systems with #causality and #RL?

Apply to our PhD position in Amsterdam 🚲🌷🇳🇱

Deadline: June 15

🔥 Got a great work on causal representation learning, abstraction, high-dimensional discovery, or other hot topics in causality?

🇧🇷 Don’t miss your chance to present in Rio at the CAR Workshop at #UAI2025!

⏰ Deadline is in 1 week – May 26!
🌐 sites.google.com/view/car-25/

A little over a week ago, I had the chance to attend #AISTATS and present our poster on SNAP (matyasch.github.io/snap)! Three days of brilliant invited talks and a stream of fascinating papers left me with a much longer reading list about ideas to explore.

New PhD position at the University of Amsterdam in @amlab.bsky.social on learning concepts with theoretical guarantees using #causality and #RL with me, Frans Oliehoek (TU Delft) and Herke van Hoof 💥

Deadline: 15 June

werkenbij.uva.nl/en/vacancies...

Just arrived in Phuket for #AISTATS2025. Can't wait to present our poster (in tube) about SNAP 🫰 on day 2, Sunday! Come check it out and let's chat about scalable causal discovery!

⏰ Don't miss Mátyás talk today at 3PM!

🎥 See you online meet.google.com/cqt-ufji-xfz

🤌 ...or live in the CS Department of the University of Pisa!

A few weeks ago, I presented SNAP at the wonderful #Bellairs Workshop on Causality in Barbados🐢

This Friday 🫰meets 🤌 as I will get to present SNAP again at the kick-off of the newest season of @causalclub.di.unipi.it! Check out this, and their other amazing upcoming talks at causalclub.di.unipi.it

10/10 SNAP is joint work with a fantastic team of Tom Claassen and @smaglia.bsky.social. Visit our project page on matyasch.github.io/snap/, run SNAP using our publicly available code at github.com/matyasch/snap, and visit to our poster at #aistats2025! 🏖️

9/10 We also evaluate SNAP on semi-synthetic settings including data generated from the MAGIC-NIAB network, which captures genetic effects and phenotypic interactions 🧬 We see that SNAP greatly reduces the number of CI tests and execution time compared to most baselines.

8/10 Many non-ancestors are already identified by marginal tests, enabling prefiltering with SNAP(0) to significantly speed up computation time. Increasing the number of prefiltering iterations k further reduces the number of CI tests needed, especially in dense graphs 🧶

7/10 SNAP(∞) consistently ranks among the best in the number of CI tests and computation time across all domains, while maintaining a comparable intervention distance. In contrast, other methods vary in performance depending on the setting 🚀

6/10 We can also run SNAP until completion, to obtain a stand-alone causal discovery algorithm, called SNAP(∞). SNAP(∞) is sound and complete over the possible ancestors of targets ✅ Thus, unlike previous work on local causal discovery, it finds efficient adjustment sets.

5/10 SNAP is straightforward to combine with readily available causal discovery algorithms 🧩 We can simply stop it at any maximum iteration k and run another algorithm on the remaining variables. We refer to this approach as prefiltering with SNAP(k).

4/10 To solve this task, we show that only possible ancestors of the targets are required to identify their causal relationships and efficient adjustment sets💡 Driven by this, we propose SNAP to progressively prune non-ancestors, leading to much fewer higher order CI tests.

3/10 We formalize this as the task of “targeted causal effect estimation with an unknown graph”, which focuses on identifying causal effects between a small set of target variables in a ✨computationally and statistically efficient way✨

2/10 Discovering causal relations can help us estimate causal effects, but it is expensive 📈 If we are only interested in estimating the causal effects between a few target variables, can we instead only discover a subgraph that includes these targets and their adjustment sets?

Do you want to estimate causal effects for a small set of target variables without knowing the causal graph, but discovering it takes too long? Now you can get adjustment sets in a SNAP🫰accepted at #aistats2025!

📜 arxiv.org/abs/2502.07857
🧩 matyasch.github.io/snap/
🧵 1/10

Professor Imbens also had a mentoring session with our PhD students actively working on causality, discussing their ideas and the potential impact of their applications! 👨‍🔬👩‍🔬

@matyasch.bsky.social @roelhulsman.bsky.social @rmassidda.it @danruxu.bsky.social 🔥

Congrats to @smaglia.bsky.social for now being an ELLIS Scholar! 🤩🥳🎉

if you ever wondered how to concisely represent causal models, don’t miss my talk on causal abstraction tomorrow at @ellisamsterdam.bsky.social