Business and Economic Statistics Student Paper Awards

Network data enriched with textual information, referred to as text networks, arise in a wide range of applications, including email communications, scientific collaborations, and legal contracts. In such settings, both the structure of interactions (i.e., who connects with whom) and their content (i.e., what is communicated) are useful for understanding network relations. Traditional network analyses often focus only on the structure of the network and discard the rich textual information, resulting in an incomplete or inaccurate view of interactions. In this paper, we introduce a new modeling approach that incorporates texts into the analysis of networks using topic-aware text embedding, representing the text network as a generalized multi-layer network where each layer corresponds to a topic extracted from the data. We develop a new and flexible latent space network model that captures how node-topic preferences directly modulate edge formation, and establish identifiability conditions for the proposed model. We tackle model estimation with a projected gradient descent algorithm, and further discuss its theoretical properties. The efficacy of our proposed method is demonstrated through simulations and an analysis of an email network.  

Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in interference-aware settings, the effect of interference in CB, as well as the underlying theory, remains significantly underexplored. In this paper, we introduce a systematic framework to address interference in Linear CB (LinCB), bridging the gap between causal inference and online decision-making. We propose a series of algorithms that explicitly quantify the interference effect in the reward modeling process and provide comprehensive theoretical guarantees, including sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The effectiveness of our approach is demonstrated through simulations and a synthetic data generated based on MovieLens data. 

Linear-in-means models are often used to investigate peer effects. Estimating peer effects in linear-in-means models requires care, as peer effects may be subject to the ``reflection problem,'' an identification failure in the form of perfect collinearity. In many settings, well-known identification conditions guarantee that perfect collinearity is not an issue. However, these identifying conditions are not sufficient to guarantee that peer effects are estimable. Even when identifying conditions guarantee that peer effect terms are not collinear, peer effects can become increasingly collinear as sample size grows larger. We show that asymptotic collinearity occurs whenever nodal covariates are independent of the network and the minimum degree of the network is growing. Asymptotic collinearity can cause estimates of peer effects to be inconsistent or to converge at slower than expected rates. We also show that dependence between nodal covariates and network structure can alleviate collinearity issues in random dot product graphs. These results suggest that linear-in-means models are less reliable for studying peer influence than previously believed. 

With the growing demand for personalized assortment recommendations, concerns over data
privacy have intensified, highlighting the urgent need for effective privacy-preserving strategies.
This presentation presents a novel framework for privacy-preserving dynamic assortment selection
using the multinomial logit (MNL) bandits model. Our approach employs a perturbed
upper confidence bound method, integrating calibrated noise into user utility estimates to
balance between exploration and exploitation while ensuring robust privacy protection. We
rigorously prove that our policy satisfies Joint Differential Privacy (JDP), which better suits
dynamic environments than traditional differential privacy, effectively mitigating inference
attack risks. This analysis is built upon a novel objective perturbation technique tailored for
MNL bandits, which is also of independent interest. Theoretically, we derive a near-optimal
regret bound for our policy and explicitly quantify how privacy protection impacts
regret. Through extensive simulations and an application to the Expedia hotel dataset, we
demonstrate substantial performance enhancements over the benchmark method.