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arXiv AI By Wenxin Chen, Kyra Gan, Mingyu Lu, Zilin Jing, Fei Wang, Hoifung Poon, Tristan Naumann, Peniel Argaw

A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation

Read the original on arXiv AI →

The paper introduces PICLE-PFN, a foundation model for longitudinal causal effect estimation that handles dynamic treatment policies. It represents policies via in-context history-decision demonstrations and is pretrained on synthetic data from longitudinal structural causal models, enabling it to predict conditional average potential outcomes in a single forward pass without task-specific training. Experiments on synthetic and semi-synthetic benchmarks show competitive RMSE and better uncertainty calibration while reducing deployment runtime compared to conventional estimators.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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