🚨 This is the future of causal inference. 🚨👇 CausalPFN is a foundation model trained on simulated causal worlds—it estimates heterogeneous treatment effects in-context from observational data. No retraining. Just inference. A 𝘮𝘢𝘴𝘴𝘪𝘷𝘦 leap forward for the field. 🚀

1/7 🚀 Thrilled to announce that our paper ExOSITO: Explainable Off-Policy Learning with Side Information for ICU Lab Test Orders has been accepted to #CHIL2025! Please feel free to come by my poster session this Thursday to chat. #MedAI #HealthcareAI

How do we reimagine healthcare systems with AI that are approx. correct, but rapidly improving? Last August, Toronto hosted #MLHC24. ✨We had clinicians & engineers work together to find errors in LLMs without assuming bad intent. See Michael Cooper @ ICML's highlights👇 🧵1/5

🚀 Open-source + open dataset!! Going to be a fun weekend.

💲😢Work on predictive problems where samples are scarce, and labels are expensive? Check out AutoElicit! 🔢Use an LLM to extract prior distributions over the parameters of a predictive model. ⏳Save ~6 months of labelling effort on real outcomes in dementia care.