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 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