CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

TL;DR

CausalPFN is a transformer-based model trained on simulated data to directly estimate causal effects from observational data, eliminating the need for manual estimator selection and tuning.

Contribution

It introduces a single, pre-trained transformer that generalizes causal effect estimation across diverse datasets without task-specific adjustments.

Findings

Outperforms existing methods on benchmark datasets

Provides calibrated uncertainty estimates

Effective in real-world policy and uplift modeling tasks

Abstract

Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands substantial manual effort and domain expertise. We present CausalPFN, a single transformer that amortizes this workflow: trained once on a large library of simulated data-generating processes that satisfy ignorability, it infers causal effects for new observational datasets out of the box. CausalPFN combines ideas from Bayesian causal inference with the large-scale training protocol of prior-fitted networks (PFNs), learning to map raw observations directly to causal effects without any task-specific adjustment. Our approach achieves superior average performance on heterogeneous and average treatment effect estimation benchmarks (IHDP, Lalonde, ACIC). Moreover, it shows competitive performance for real-world policy making on uplift modeling tasks. CausalPFN provides calibrated uncertainty estimates to support reliable decision-making based on Bayesian principles. This ready-to-use model requires no further training or tuning and takes a step toward automated causal inference (https://github.com/vdblm/CausalPFN/).