There's been a lot of success in causal effect estimation using machine learning. But what if point identification is impossible? Our NeurIPS 2022 paper, "Partial Identification of Treatment Effects with Implicit Generative Models," estimates bounds on causal effects instead. 🧵

📢 New Paper at #UAI2023! 🎉 Dependent censoring presents a *common and substantial* source of bias in modern survival analysis. We show how to use copulas to mitigate this bias. arxiv.org/abs/2306.11912 So what is dependent censoring? And why is it problematic? 🧵👇 (1/n)

Can neural networks learn to map from observational datasets directly onto causal effects? YES! Introducing CausalPFN, a foundation model trained on simulated data that learns to do in-context heterogeneous causal effect estimation, based on prior-fitted networks (PFNs). Joint