Emanuela Dreassi, Fabrizio Leisen, Luca Pratelli, Pietro Rigo
Two misunderstandings, frequently arising in Bayesian predictive inference, are discussed. The first deals with the data generating mechanism, while the second consists in overestimating the role played by asymptotic exchangeability. Some consequences of such misunderstandings are highlighted through examples.
Causal subgroup analyses often report a small number of groups summarizing treatment effect heterogeneity, as if that number were a well-defined estimand. Outside genuinely latent class populations, however, a ``true'' subgroup count is model dependent rather than a population fu…
We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framework. In the two-step method, vector search retrieves action-specific neighboring evidence in an embedd…
Language models produce probabilities over words, but professional decisions require uncertainty over meaningful states such as diagnoses, hypotheses or operational conditions. A model's printed numerical confidence does not establish reliability. We introduce a semantic map: a p…
Blanka Horvath, Wen Su, Wu Su, Binnan Wang, Ruixun Zhang
Many prediction and decision-making problems in operations research involve path-valued covariates -- data that evolve over time -- for which path signatures have become a canonical feature representation. Their use is justified by a universal approximation theorem, but this is a…
Consider the partial linear model $Y = μ_0(X) + β_0 \cdot T + \varepsilon$ and $T = π_0(X) + u$ in the structure-agnostic setting, where we are blind to the structure $μ_0$ and $π_0$ and estimate the nuisances by a black-box hypothesis class. The learnability of the class is char…
Yi Li, Ashkan Ertefaie, Mark van der Laan
For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well understood for smooth parametric estimators, its theoretical properties for many modern semiparametric…