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Florida State University

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Research library335linked papers
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Selected work

Representative Papers

Learning Probabilities of Causation from Finite Population Data

Oct 16, 2022arXiv.org

Accurately estimating population-natural-stratum (PNS), probability of sufficiency (PS), and probability of necessity (PN) under limited observational or experimental data remains challenging due to reliance on full subgroup distribution assumptions. Method: This paper introduces the first machine learning framework for tight causal probability bound inference, integrating Tian–Pearl theoretical bounds with supervised learning over subgroup feature embeddings. Leveraging data from only ~500 observable subgroups, the method generalizes tightly bounded PNS estimates to 32,768 latent subgroups. Contribution/Results: The framework significantly reduces data requirements compared to conventional distribution-dependent approaches, enhancing feasibility of causal interpretability in small-sample settings. Its core innovation is establishing a novel “causal bound learning” paradigm—replacing the classical assumption of complete distributional knowledge with learnable, embedding-based generalization. This advances fine-grained causal assessment under finite-data constraints and opens new avenues for scalable, data-efficient causal inference.

7 citationsRead paper

Persuasion with Ambiguous Communication

Jul 08, 2024ACM Conference on Economics and Computation

This paper investigates whether a sender can enhance persuasion effectiveness through ambiguous communication under ambiguity aversion. Method: Extending the Bayesian persuasion framework, we incorporate the smooth ambiguity model of Klibanoff et al. (2005) and systematically characterize—using concavification techniques and incentive-compatible signal design—the necessary and sufficient conditions for effective ambiguous communication. Contribution/Results: We establish that ambiguous communication never improves the sender’s payoff in binary-action settings; however, in settings with three or more actions, Pareto-ranked experimental split structures enable substantial sender gains. Crucially, these gains are robust to perturbations in the receiver’s degree of ambiguity aversion. Our findings provide novel theoretical foundations and design principles for real-world ambiguous decision-making contexts—such as bank stress testing—where ambiguity is pervasive and agents exhibit systematic ambiguity aversion.

3 citations1 influentialRead paper

RULERS: Locked Rubrics and Evidence-Anchored Scoring for Robust LLM Evaluation

Jan 13, 2026

Large language models (LLMs) often exhibit unreliable performance as automated evaluators due to prompt sensitivity, unverifiable reasoning, and misalignment with human rating scales. To address these limitations, this work proposes compiling natural language scoring rubrics into executable specifications, integrating structured decoding, deterministic evidence anchoring, and a lightweight Wasserstein-based post-calibration mechanism—all without updating model parameters. This approach yields stable, auditable evaluations that significantly improve agreement with human judgments on essay and summarization tasks, demonstrate strong robustness against adversarial perturbations, and enable smaller models to match or even surpass the evaluation performance of much larger counterparts.

1 citationsRead paper

The Z-Gromov-Wasserstein Distance

Aug 15, 2024arXiv.org

Existing similarity measures for complex structured data—such as attributed graphs and higher-order relational data—lack a unified, mathematically rigorous, and computationally tractable framework. Method: We propose the Z-Gromov–Wasserstein (Z-GW) distance, a generalization of the Gromov–Wasserstein (GW) distance to measure spaces endowed with Z-valued kernels (Z-networks), where Z is an arbitrary metric space. Contribution/Results: We establish the first general GW theory parameterized by Z, proving that Z-GW is a well-defined metric inheriting separability, completeness, and geodesicity from Z. We derive a tight, computationally feasible lower bound and design efficient relaxation-based optimization and approximation algorithms. Compared to existing GW variants, Z-GW offers greater theoretical unification and practical scalability, providing a mathematically sound yet computationally viable similarity measure for attributed graphs and other complex structured data.

1 citationsRead paper

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking

Jun 07, 2024

To address computational challenges in modeling non-Gaussian spatiotemporal data—specifically, the intractability of analytically integrating random effects and weak parameter identifiability impeding MCMC convergence—this paper proposes a Bayesian inference framework based on predictive stacking. Innovatively integrating generalized conjugate multivariate distribution theory with an extended Diaconis–Ylvisaker conjugate prior family, the method enables exact sampling from conditional posteriors and facilitates Bayesian model integration across disparate model configurations. It circumvents the computational bottlenecks inherent in conventional generalized linear mixed models, where closed-form integration of random effects is infeasible. Simulation studies demonstrate substantially improved MCMC convergence speed and parameter estimation accuracy relative to standard MCMC approaches. The framework is further validated on large-scale spatiotemporal count data from the North American Breeding Bird Survey, confirming its robustness and scalability.

1 citationsRead paper
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