Sara Magliacane

Estimating causal effects efficiently doesn’t have to mean discovering the entire causal graph! Now you can find the optimal adjustment from only local information using LOAD!

📜 Preprint: arxiv.org/abs/2510.14582
👾 Code: github.com/Matyasch/load
🧵 1/8

Interesting new paper on “A Dozen Challenges in Causality and Causal Inference.” After 30+ years of progress in causal inference, the authors lay out the biggest challenges in current research. Good teaching resource!

@guidoimbens.bsky.social @smaglia.bsky.social

arxiv.org/pdf/2508.170...

The Risks of Risk Assessment: Causal Blind Spots When Using Prediction Models for Treatment Decisions | Annals of Internal Medicine Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already receiv...

New paper in @annalsofim.bsky.social

"50 ways to misinterpret clinical prediction models for treatment decisions”

--> Published version: www.acpjournals.org/doi/10.7326/...

--> Open access version: arxiv.org/pdf/2402.17366

and finally on The 1st Workshop on Safe AI

sites.google.com/view/safeai2...

then on The Workshop on Causal Abstractions and Representations #CAR25

sites.google.com/view/car-25/

memories from the last day of #UAI2025:

the 8th Workshop on Tractable Probabilistic Modeling #TPM2025

tractable-probabilistic-modeling.github.io/tpm2025/

Grateful to the tutorial chairs @urish.bsky.social and @sineadwilliamson.bsky.social for the opportunity, and big thanks to the PC chairs @smaglia.bsky.social and @csilviavr.bsky.social for being on the ground and making UAI 2025 such a unique experience!

a huge thanks to @rmassidda.it who not only flawlessly managed to update the #UAI2025 website, but also wrote an amazing pipeline (with docker!) from scratch to do so.

All future publicity chairs will own Riccardo!

The faces behind the @auai.org PR machine and cameras:
@nolovedeeplearning.bsky.social @rmassidda.it

and to @leanderk.bsky.social @paolomorettin.bsky.social Roberto Sebastiani, @andreapasserini.bsky.social @nolovedeeplearning.bsky.social
for the ✨Best Student Paper Runner Up Award✨ for

"A Probabilistic Neurosymbolic Layer for Algebraic Constraint Satisfaction"

👉 openreview.net/forum?id=9Uk...

congratulations to Paula Cordero Encinar, Francesca Romana Crucinio and O. Deniz Akyildiz (@odakyildiz.bsky.social‬) for winning the 🏆 Best Student Paper Award 🏆 at #UAI2025 with

"Proximal Interacting Particle Langevin Algorithms"

👉 openreview.net/forum?id=rTq...

congratulations to Frédéric Koriche, Jean-Marie Lagniez and Chi Tran for winning the 🏆 Best Paper Award 🏆 at #UAI2025 for

"Probabilistic Explanations for Regression Models"

👉 openreview.net/forum?id=ipn...

Last talk of the day for TPM!

@auai.org #TPM2025

let's start day 4 with the oral session on optimization!

👉 www.auai.org/uai2025/sche...

last two orals on robustness (now posters!)

👉 www.auai.org/uai2025/sche...

we almost end the day (banquet incoming) with an extremely lively poster session!

last session of orals of the day, this time on safety!

👉 www.auai.org/uai2025/sche...

Day 3 continues with the oral presentations about Causality!

👉 www.auai.org/uai2025/sche...

we open day 3 with Elias Bareinboim (causalai.net) giving a keynote on

"Towards Causal Artificial Intelligence"

reprising some of the themes of his upcoming book (causalai-book.net)

poster session going strong 💪

oral presentations from session 2, on uncertainty

www.auai.org/uai2025/sche...

presentations from the oral session #1 on learning

www.auai.org/uai2025/sche...

oral sessions are on! 🎉

get the schedule of all talks and posters sessions at www.auai.org/uai2025/sche...

First #UAI2025 keynote by Francesca Dominici (hsph.harvard.edu/profile/fran...) on

"""
Uncertainty in AI: Solution or Obstacle for Climate Action?
"""

✨ our General chair Negar Kiyavash and Program chairs @csilviavr.bsky.social and @smaglia.bsky.social kicking off Day 2 ✨

a big thanks to our sponsors!

Huawei, Google, AI journal, DE Shaw & co and ‪@cnpq-oficial.bsky.social‬

and to our chairs @smaglia.bsky.social @csilviavr.bsky.social and Daniel Ratton Figueiredo

the conference cannot officially start without a proper reception 🍹

and

"Modern Approximate Inference: Variational Methods and Beyond"

by Diana Cai (Flatiron Institute) and Yingzhen Li (Imperial College London)

2) "Causal Inference with Deep Learning and Generative Models"

by Murat Kocaoglu (Purdue University) and Md Musfiqur Rahman (Purdue University)

we reprise our Day 1 schedule with three amazing tutorials:

1) "Introduction to Bayesian Nonparametric Methods for Causal Inference"

by Michael Daniels (U of Florida) and Jason Roy (Rutgers)