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arXiv Machine Learning By Gabe Schulman, Lucius E. J. Bynum, Yoav Wald

Unit-level aggregation breaks coverage in causal foundation models

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The paper investigates why causal foundation models (CFMs) produce poorly calibrated credible intervals when aggregating unit-level estimates over groups. It shows that CFMs combine unit-level variances assuming independence, but posterior uncertainty induces correlations that shrink the combined variance, leading to coverage loss that worsens with group size. The authors demonstrate that neural process models, which jointly predict for groups, correct this bias and markedly improve interval coverage across synthetic and benchmark datasets.

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 Machine Learning.

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