CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
This work addresses the need for automated and efficient causal inference in applications like policy-making, though it is incremental by building on prior-fitted networks and Bayesian methods.
The paper tackles the challenge of selecting appropriate causal effect estimators from observational data by introducing CausalPFN, a transformer model trained on simulated data to infer causal effects without manual tuning, achieving superior average performance on benchmarks like IHDP, Lalonde, and ACIC.
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/).