11 citations · 27 across the 15 of their papers we have counts for
10 papers · 1 filter
Offline Policy Evaluation and Optimization under Confounding
Chinmaya Kausik, Yangyi Lu, Kevin Tan +3
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning. Using conventional methods for offl…
The Sample Complexity of Online Contract Design
Banghua Zhu, Stephen Bates, Zhuoran Yang +3
We study the hidden-action principal-agent problem in an online setting. In each round, the principal posts a contract that specifies the payment to the agent based on each outcome…
Dynamic Survival Transformers for Causal Inference with Electronic Health Records
Prayag Chatha, Yixin Wang, Zhenke Wu +1
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assu…
Interventional Causal Representation Learning
Kartik Ahuja, Divyat Mahajan, Yixin Wang +1
Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (…
Data-Driven Influence Functions for Optimization-Based Causal Inference
Michael I. Jordan, Yixin Wang, Angela Zhou
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inferen…
Anticipating Performativity by Predicting from Predictions
Celestine Mendler-Dünner, Frances Ding, Yixin Wang
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict. Understanding the…
Valid Inference After Causal Discovery
Paula Gradu, Tijana Zrnic, Yixin Wang +1
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challeng…
Breaking Feedback Loops in Recommender Systems with Causal Inference
Karl Krauth, Yixin Wang, Michael I. Jordan
Recommender systems play a key role in shaping modern web ecosystems. These systems alternate between (1) making recommendations (2) collecting user responses to these recommendati…
Recommendation Systems with Distribution-Free Reliability Guarantees
Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates +2
When building recommendation systems, we seek to output a helpful set of items to the user. Under the hood, a ranking model predicts which of two candidate items is better, and we…
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma +1
Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea is to trade st…