{"ID":3005081,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T07:50:16.0004273Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03332","arxiv_id":"2606.03332","title":"Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference","abstract":"Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping that can be readily integrated with any model like a neural network or a gradient boosting algorithm. Extensive evaluations on causal inference benchmarks demonstrate that our tailored objective consistently outperforms standard likelihood-based and covariate-balancing approaches.","short_abstract":"Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bi...","url_abs":"https://arxiv.org/abs/2606.03332","url_pdf":"https://arxiv.org/pdf/2606.03332v1","authors":"[\"Roman Plaud\",\"Alexandre Perez-Lebel\",\"Antoine Saillenfest\",\"Thomas Bonald\",\"Marine Le Morvan\",\"Gaël Varoquaux\",\"Matthieu Labeau\"]","published":"2026-06-02T08:41:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}