183 citations · 521 across the 26 of their papers we have counts for
27 papers
Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome
Shuozhi Zuo, Yixin Wang, Fan Yang
Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational stu…
Posterior Collapse and Latent Variable Non-identifiability
Yixin Wang, David M. Blei, John P. Cunningham
Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network.…
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
Linying Zhang, Lauren R. Richter, Yixin Wang +4
Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While v…
Partial Identification with Noisy Covariates: A Robust Optimization Approach
Wenshuo Guo, Mingzhang Yin, Yixin Wang +1
Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, o…
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
Yixin Wang, Anthony Degleris, Alex H. Williams +1
Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural…
Conformal Sensitivity Analysis for Individual Treatment Effects
Mingzhang Yin, Claudia Shi, Yixin Wang +1
Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an as…
Adjusting for indirectly measured confounding using large-scale propensity scores
Linying Zhang, Yixin Wang, Martijn Schuemie +2
Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions f…
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang +1
We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors (representations) which summarize the ass…
Optimization-based Causal Estimation from Heterogenous Environments
Mingzhang Yin, Yixin Wang, David M. Blei
This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is th…
Desiderata for Representation Learning: A Causal Perspective
Yixin Wang, Michael I. Jordan
Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing…
Learning Equilibria in Matching Markets from Bandit Feedback
Meena Jagadeesan, Alexander Wei, Yixin Wang +2
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of…
Multi-Source Causal Inference Using Control Variates
Wenshuo Guo, Serena Wang, Peng Ding +2
While many areas of machine learning have benefited from the increasing availability of large and varied datasets, the benefit to causal inference has been limited given the strong…
Point process models for sequence detection in high-dimensional neural spike trains
Alex H. Williams, Anthony Degleris, Yixin Wang +1
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a long…
Towards Clarifying the Theory of the Deconfounder
Yixin Wang, David M. Blei
Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm. The paper discusses theoretical requirements and presents empirical studies. Several…
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
Yixin Wang, David M. Blei
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805.06826). Many of their remarks are interesting. But they also clai…
Multiple Causes: A Causal Graphical View
Yixin Wang, David M. Blei
Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non…
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Yixin Wang, Dhanya Sridhar, David M. Blei
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past de…
Large Sample Properties of Matching for Balance
Yixin Wang, José R. Zubizarreta
Matching methods are widely used for causal inference in observational studies. Among them, nearest neighbor matching is arguably the most popular. However, nearest neighbor matchi…
Variational Bayes under Model Misspecification
Yixin Wang, David M. Blei
Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees, most…
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
Linying Zhang, Yixin Wang, Anna Ostropolets +3
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Rece…
Using Embeddings to Correct for Unobserved Confounding in Networks
Victor Veitch, Yixin Wang, David M. Blei
We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connect…
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Yixin Wang, Dawen Liang, Laurent Charlin +1
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question--…
Black Box FDR
Wesley Tansey, Yixin Wang, David M. Blei +1
Analyzing large-scale, multi-experiment studies requires scientists to test each experimental outcome for statistical significance and then assess the results as a whole. We presen…
The Blessings of Multiple Causes
Yixin Wang, David M. Blei
Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studi…