Special thanks to my co-authors @davidruegamer.bsky.social and @tnagler.bsky.social—really enjoyed this collaboration!

In this work, we explore how pre-trained neural networks can be leveraged to adjust for confounding in treatment effect estimation involving complex data such as images or text—providing a principled approach to integrate non-tabular data into causal effect estimation.

Looking forward to presenting our paper "Adjustment for Confounding using Pre-Trained Representations" at #ICML2025 in Vancouver next week! 🎉🇨🇦

Feel free to check out our paper and reach out if you're attending or would like to discuss!

📄 Paper: arxiv.org/abs/2506.14329

MCML Researchers With 22 Papers at ICML 2025 We are happy to announce that MCML researchers are represented with 22 papers at ICML 2025.

MCML researchers will be represented at #ICML2025 with more than 20 accepted paper 🎉

Check them out: mcml.ai/news/2025-07...

It has been a great honour to presented our paper "Additive Model Boosting: New Insights and Path(ologie)s" at #AISTATS 2025!🚀 Joint work with @davidruegamer.bsky.social!

Check out the paper and feel free to reach out:
🔗 arxiv.org/abs/2503.05538

It seems that we have 3 accepted papers at ICML 2025 🔥

Congratulations to the #AABI2025 Workshop Track Outstanding Paper Award recipients!

Arriving in Singapore this afternoon 🛬 I'll attend #ICLR2025, #AABI2025, and #AISTATS2025 together with many of my students and collaborators to present our 2 orals, 5 posters, and 14 workshop contributions 🚀

Feel free to drop by!

🔥🔥 Two papers accepted at #AISTATS2025 💪

- Oral: *Additive Model Boosting: New Insights and Path(ologie)s* by @rickmer.bsky.social
- Poster: *Paths and Ambient Spaces in Neural Loss Landscapes* from Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, Oliver Dürr