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Researcher profile

Tian Lu

Tian Lu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningApplicationsArtificial Intelligenceecon.GNMethodologyq-fin.ECQuantitative Methods

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Published work

4 published item(s)

preprint2026arXiv

The Impact of Heatwaves on Population Health: A Large Language Model-Enhanced Agent-Based Simulation

Extreme heat events are increasing in frequency and intensity under climate change, but the socio-behavioral mechanisms that shape community resilience remain insufficiently understood. This study uses a Large Language Model-enhanced agent-based model to simulate responses to a prolonged heatwave in a virtual society. One hundred heterogeneous agents were assigned a Heat Vulnerability Index based on demographic risk factors and observed over 13 simulated days covering baseline, heatwave, and recovery periods. The simulation shows that heat-related impacts are primarily psychosocial and unequally distributed. Agents with higher vulnerability experienced larger declines in perceived safety and social connection than agents with lower vulnerability. Vulnerability also shaped adaptive capacity. More resilient agents maintained routine self-care and protective behaviors, whereas highly vulnerable agents showed behavioral constriction, marked by reduced engagement in protective actions. At the collective level, risk-information diffusion followed a pattern of complex contagion, with adoption driven more by repeated social reinforcement within cohesive networks than by broad exposure alone. These findings suggest that LLM-enhanced simulation can help identify behavioral and social mechanisms of climate resilience and inform heat-risk interventions that combine targeted support for vulnerable groups with community-based information pathways.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Uncovering the Source of Machine Bias

We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials

This research was motivated by studying anti-drug antibody (ADA) formation and its potential impact on long-term benefit of a biologic treatment in a randomized controlled trial, in which ADA status was not only unobserved in the control arm but also in a subset of patients from the experimental treatment arm. Recent literature considers the principal stratum estimand strategy to estimate treatment effect in groups of patients defined by an intercurrent status, i.e. in groups defined by a post-randomization variable only observed in one arm and potentially associated with the outcome. However, status information might be missing even for a non-negligible number of patients in the experimental arm. For this setting, a novel weighted principal stratum approach is presented: Data from patients with missing intercurrent event status were re-weighted based on baseline covariates and additional longitudinal information. A theoretical justification of the proposed approach is provided for different types of outcomes, and assumptions allowing for causal conclusions on treatment effect are specified and investigated. Simulations demonstrated that the proposed method yielded valid inference and was robust against certain violations of assumptions. The method was shown to perform well in a clinical study with ADA status as an intercurrent event.

0 citations0 reviewsNo code linkedOpen access