We demonstrate that the PFN-framework allows for the accurate estimation of causal effects under weakened assumptions.

CausalPFN is a pre-trained transformer that amortizes causal effect estimation: trained once on simulated data-generating processes, it outputs calibrated effects for new observational datasets with zero tuning.