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Unit-level aggregation breaks coverage in causal foundation models

Schulman, Gabe et al.
arXiv (All) · Papers
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machine-learning
Causal foundation models (CFMs) show promising results for treatment-effect estimation, with unit-level estimates that are competitive on standard benchmarks. Many causal analyses, however, inform decisions through effects averaged over groups of units, such as the full sample, the treated units, or
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