arXiv:2604.02403v1 Announce Type: cross
Abstract: This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables — specifically variables that describe …
arXiv:2603.24705v2 Announce Type: replace-cross
Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, l…
arXiv:2604.02969v1 Announce Type: cross
Abstract: The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper proposes an inversion-free stochastic natural gradie…
arXiv:2402.01207v5 Announce Type: replace
Abstract: We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries whi…
arXiv:2604.02739v1 Announce Type: cross
Abstract: Latent space models are widely used in statistical network analysis and are often fit by Markov chain Monte Carlo. However, posterior summaries of latent coordinates are not canonical because the likel…
arXiv:2604.02886v1 Announce Type: cross
Abstract: We study high-dimensional mediation analysis in which exposures, mediators, and outcomes are all multivariate, and both exposures and mediators may be high-dimensional. We formalize this as a many (exp…
arXiv:2604.02678v1 Announce Type: cross
Abstract: Clinical evidence synthesis requires identifying relevant trials from large registries and aggregating results that account for population differences. While recent LLM-based approaches have automated …
arXiv:2405.03083v5 Announce Type: replace-cross
Abstract: Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup s…
arXiv:2510.00463v4 Announce Type: replace-cross
Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false discovery rate…
arXiv:2510.18520v2 Announce Type: replace
Abstract: The ROC curve is widely used to assess binary classifiers. Yet for some applications, such as alert systems for monitoring hospitalized patients, conventional ROC analysis cannot meet two key deploym…