It’s a treat going from SDM 2025 to CLeaR 2025 and seeing two excellent keynotes at the intersection of social systems and #causal modeling. @jugander.bsky.social on interrupting misinformation with community notes and @bleilab.bsky.social on hierarchical causal models.

Thoroughly enjoyed participating in and presenting at the 2025 Data Science (Academic) Leadership Summit, learning more about the data science and AI landscape across universities, and feeling inspired by the enriching interactions. Many thanks to the organizers!

If you are at #aaai2025, come by our poster on “Learning Exposure Mapping Functions for Peer Effect Estimation” at the AAAI Workshop on AI with Causal Techniques, presented by my PhD student Shishir Adhikari, to learn how we automatically account for different counterfactual ego network structures.

@rebeccasaxe.bsky.social giving an informative and thought-provoking talk on theory of mind at the #aaai2025 workshop on Advancing Artificial Intelligence through Theory of Mind (ToM4AI).

Looking forward to attending AAAI this week. Will any of you be there?

Day 2 of the GraPFiCs workshop starting w/ Kristen Altenburger‘s talk on Graphs for Product Innovation: Explanation vs. Prediction Problems tying nicely causality, fairness, privacy and graphs along the explanation vs. prediction spectrum of modeling.

sites.google.com/view/grapfic...

An exciting lineup of speakers at the workshop on Foundations of Fairness, Privacy and Causality in Graphs!

sites.google.com/view/grapfic...

I can't help but notice that most of the AAAS S&TP Fellows in AI are women 💪. Watch Kiri Wagstaff, Serena Booth, Cynthia Lee, Rebecca Voglewede, and Soribel Feliz, as they help Congress navigate the complex connections between AI and policy.

One of my favorite things about being in academia is learning from students and being inspired by their curiosity and ideas. #gradingprojectproposals

The Harvard Data Science Initiative has launched its application for the 2024–2025 Postdoctoral Fellowship. Share with anyone who is a good fit; and have them reach out if their interest aligns with mine (e.g., causal inference, fairness, model validation, anomaly detection, GenAI, etc.).

Thanks for the new follows! I don’t know many of you 😳 Feel free to say ‘hi’ here and tell me a bit about your work 😊

How far can we go in estimating heterogeneous treatment effects when the causal model is unknown a priori? Check out our survey paper on "Data-driven estimation of heterogeneous treatment effects": arxiv.org/abs/2301.06615.

I am teaching a seminar course on Causal Inference and Learning again this semester. I change the paper selection every year and sometimes repeat some. Last year, I focused mostly on causal inference with interference. You can see the list of papers we discussed here: www.cs.uic.edu/~elena/cours...

The sky is always bluer on the other side.