{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/causalpfn-amortized-causal-effect-estimation","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","arxiv_id":"2506.07918","date":"2025-06-09","proceeding":null,"authors":["Vahid Balazadeh","Hamidreza Kamkari","Valentin Thomas","Benson Li","Junwei Ma","Jesse C. Cresswell","Rahul G. Krishnan"],"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 does not require any further training or tuning and takes a step toward automated causal inference (https://github.com/vdblm/CausalPFN).","url_abs":"https://arxiv.org/abs/2506.07918v1","url_pdf":"https://arxiv.org/pdf/2506.07918v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"causalpfn-amortized-causal-effect-estimation","repo_url":"https://github.com/vdblm/CausalPFN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"heterogeneous-treatment-effect-estimation","task_name":"Heterogeneous Treatment Effect Estimation"},{"task_slug":"in-context-learning","task_name":"In-Context Learning"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"},{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/heterogeneous-treatment-effect-estimation-on","task":"Heterogeneous Treatment Effect Estimation","dataset":"IHDP","model":"CausalPFN","rank_in_archive_order":1,"of":2,"metrics":{"PEHE":"0.58"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.07918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.07918"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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