Wasserstein Causal Forests for Distribution-Valued Outcomes
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2607. 23721v1 Announce Type: cross Abstract: Distributional random forests replace mean-based CART splitting with criteria that compare the full conditional response distribution in candidate children.
arXiv:2004. 10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT).
arXiv:2609.07944v1 Announce Type: new Abstract: Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recove...
The paper investigates whether reader-specific differences in retrieval‑augmented generation (RAG) reflect reusable structure or merely input‑local interactions. By fixing query, evidence, task, scoring, and intervention, the authors find that nine readers disagree on the effect sign in 33% of cases, with reader×query interactions explaining 29.8% of utility variance. They further decompose heterogeneity into evidence activity, ordinal preference, and conditional signed direction, discovering that ordinal reader geometry is stable across multiple settings while signed geometry is task‑bounded, yet stable ordinal similarity does not predict cross‑reader intervention transfer.
arXiv:2606. 07560v1 Announce Type: cross Abstract: Function-vector (FV) heads (Todd et al.
The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.