Simulation study finds TabPFN-based causal estimators biased and slow while CausalPFN shows poor credible-interval coverage in real-world-like scenarios.
Automating the practice of science: Opportunities, challenges, and implicationsProceedings of the National Academy of Sciences.2025;122:e2401238121
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Prior-Data Fitted Networks for Causal Inference: a Simulation Study with Real-World Scenarios
Simulation study finds TabPFN-based causal estimators biased and slow while CausalPFN shows poor credible-interval coverage in real-world-like scenarios.