Institution profile

University of Chicago

Academic institutionnorthamerica · us
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Research library1,263linked papers
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Selected work

Representative Papers

Ratio of Mediator Probability Weighting for Estimating Natural Direct and Indirect Effects

Jun 03, 2025

This paper addresses the sensitivity of natural direct and indirect effect estimation to outcome model misspecification in settings with treatment–mediator interactions and high-dimensional confounders. We propose a fully nonparametric mediation decomposition method. Our core contribution is the first development of a *ratio-of-mediator-probability-weighting* (RMPW) framework, which entirely avoids reliance on outcome modeling, imposes no assumptions about treatment–mediator interaction, and makes no parametric or functional-form assumptions about the outcome distribution. The method integrates propensity-score-based stratified nonparametric weight estimation, nonparametric approximation of counterfactual mediator distributions, and marginal mean weighting to ensure unbiasedness, robustness, and consistency. It accommodates large-scale pre-treatment covariates and has been implemented in open-source Stata and R packages. By eliminating outcome-model dependence and relaxing key identification assumptions, our approach substantially broadens the applicability of causal mediation analysis to complex real-world settings.

68 citations12 influentialRead paper

Non-negative matrix factorization algorithms greatly improve topic model fits

May 27, 2021arXiv.org

Traditional topic models impose a “sum-to-one” constraint on parameters, leading to complex optimization landscapes and low computational efficiency. This work proposes integrating nonnegative matrix factorization (NMF) into topic model parameter estimation, eliminating the hard simplex constraint and instead leveraging NMF’s nonnegativity and low-rank structure to implicitly model topic distributions—thereby substantially simplifying the optimization problem. Methodologically, we design an efficient solving framework built upon state-of-the-art NMF algorithms and incorporate a post-processing step to recover interpretable probabilistic parameters; the method is implemented in the R package *fastTopics*. Experiments demonstrate consistent improvements in both accuracy and speed under maximum likelihood estimation and variational inference: superior fit within fixed time budgets, or significantly reduced runtime at equivalent accuracy. To our knowledge, this is the first systematic integration of NMF’s optimization advantages across the entire topic modeling pipeline, offering a new paradigm for high-dimensional text modeling that balances theoretical simplicity with computational scalability.

26 citations1 influentialRead paper

Treatment Allocation under Uncertain Costs

Mar 20, 2021

This paper addresses the problem of optimal treatment allocation under a budget constraint when treatment costs vary heterogeneously with covariates. We propose a threshold rule based on a priority score and establish, for the first time, a theoretical link between optimal allocation under uncertain costs and instrumental variable (IV) estimation of heterogeneous treatment effects. We rigorously derive the optimal threshold structure and prove its learnability. Our method integrates randomized controlled trial data, priority score modeling, threshold-based decision making, and an IV estimation framework. Empirically, the approach significantly outperforms standard benchmarks across multiple evaluation metrics, achieving maximal social value or firm profit within budget constraints. It provides a new paradigm—statistically rigorous yet practically implementable—for applications including scarce healthcare resource allocation and dynamic pricing.

14 citations1 influentialRead paper

Double Robustness of Local Projections and Some Unpleasant VARithmetic

May 01, 2024Social Science Research Network

This paper investigates coverage robustness of impulse response inference in locally misspecified vector autoregression (VAR) models. We find that conventional VAR confidence intervals exhibit severe undercoverage—even for statistically subtle, theoretically admissible misspecifications—when lag orders are short to moderate. In contrast, local projection (LP) confidence intervals demonstrate double robustness: they maintain nominal coverage under strong misspecification and asymptotically match or exceed VAR performance under weak misspecification. We establish, for the first time, that LP possesses a semilinear-regression–like double-robust structure and rigorously prove that VAR inference achieves asymptotic robustness only as the lag order diverges to infinity. Asymptotic expansions and Monte Carlo simulations confirm that LP consistently sustains nominal coverage across diverse misspecification regimes, whereas VAR exhibits substantially deflated coverage—and narrower, misleadingly precise intervals—under standard lag selections.

14 citationsRead paper

From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding

Dec 09, 2024arXiv.org

LVLMs frequently suffer from hallucinations and unreliable outputs due to misinterpretation of visual inputs. To address this, we propose an uncertainty-guided inference-time visual token dropout method: (1) the first adaptation of dropout to the visual token level during inference; (2) decoupled modeling of epistemic and aleatoric uncertainty, with explicit focus on quantifying perceptual errors; (3) uncertainty estimation via projection of visual tokens into the text embedding space, followed by weighted masking; and (4) robust, training-free correction via multi-context masked decoding and ensemble prediction. Evaluated on CHAIR, THRONE, and MMBench, our method significantly reduces object hallucination (OH) while substantially improving output reliability and cross-scenario generation quality.

13 citationsRead paper
Recent publications

Latest Papers

Mapping AI Economic Complexity

Sep 15, 2026

该研究通过分析2007-2023年的出口数据，使用经济复杂性框架评估各国在AI相关商品上的生产能力及多样化潜力。

0 citationsRead paper