ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Read the original on arXiv Machine Learning →ProximalFM is a transformer‑based model that uses prior‑data fitted networks (PFNs) to perform Bayesian proximal causal inference under hidden confounding. By training on synthetic data generated from structural causal models with oracle counterfactuals, it amortizes the Bayesian operator inversion into a single forward pass, producing posterior estimates of the conditional average treatment effect (CATE). The approach consistently outperforms prior methods across various proximal regimes, especially when latent confounding is strong and proxy variables are weakly informative, and it requires no dataset‑specific tuning.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.