Reweighted Data for Robust Probabilistic Models. - Yixin Wang 002; Alp Kucukelbir; David M. Blei (CoRR, 2016)

Evaluating Bayesian Models with Posterior Dispersion Indices. - Alp Kucukelbir; Yixin Wang 002; David M. Blei (ICML, 2017)

Robust Probabilistic Modeling with Bayesian Data Reweighting. - Yixin Wang 002; Alp Kucukelbir; David M. Blei (ICML, 2017)

Frequentist Consistency of Variational Bayes. - Yixin Wang 002; David M. Blei (CoRR, 2017)

Black Box FDR. - Wesley Tansey; Yixin Wang 002; David M. Blei; Raul Rabadan (ICML, 2018)

The Blessings of Multiple Causes. - Yixin Wang 002; David M. Blei (CoRR, 2018)

The Deconfounded Recommender: A Causal Inference Approach to Recommendation. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (CoRR, 2018)

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. - Linying Zhang; Yixin Wang 002; Anna Ostropolets; Jami J. Mulgrave; David M. Blei; George Hripcsak (MLHC, 2019)

Using Embeddings to Correct for Unobserved Confounding in Networks. - Victor Veitch; Yixin Wang 002; David M. Blei (NeurIPS, 2019)

Variational Bayes under Model Misspecification. - Yixin Wang 002; David M. Blei (NeurIPS, 2019)

Using Embeddings to Correct for Unobserved Confounding. - Victor Veitch; Yixin Wang 002; David M. Blei (CoRR, 2019)

The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs). - Linying Zhang; Yixin Wang 002; Anna Ostropolets; Jami J. Mulgrave; David M. Blei; George Hripcsak (CoRR, 2019)

Equal Opportunity and Affirmative Action via Counterfactual Predictions. - Yixin Wang 002; Dhanya Sridhar; David M. Blei (CoRR, 2019)

Multiple Causes: A Causal Graphical View. - Yixin Wang 002; David M. Blei (CoRR, 2019)

The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019). - Yixin Wang 002; David M. Blei (CoRR, 2019)

The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures. - Linying Zhang; Yixin Wang 002; Anna Ostropolets; Ruijun Chen; David M. Blei; George Hripcsak (AMIA, 2020)

Causal Inference for Recommender Systems. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (RecSys, 2020)

Towards Clarifying the Theory of the Deconfounder. - Yixin Wang 002; David M. Blei (CoRR, 2020)

A Proxy Variable View of Shared Confounding. - Yixin Wang 002; David M. Blei (ICML, 2021)

Bayesian Causal Inference for Real World Interactive Systems. - Nicolas Chopin; Mike Gartrell; Dawen Liang; Alberto Lumbreras; David Rohde; Yixin Wang 002 (KDD, 2021)

Learning Equilibria in Matching Markets from Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (NeurIPS, 2021)

Posterior Collapse and Latent Variable Non-identifiability. - Yixin Wang 002; David M. Blei; John P. Cunningham (NeurIPS, 2021)

Multi-Source Causal Inference Using Control Variates. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (CoRR, 2021)

Desiderata for Representation Learning: A Causal Perspective. - Yixin Wang 002; Michael I. Jordan (CoRR, 2021)

Optimization-based Causal Estimation from Heterogenous Environments. - Mingzhang Yin; Yixin Wang 002; David M. Blei (CoRR, 2021)

Identifiable Variational Autoencoders via Sparse Decoding. - Gemma E. Moran; Dhanya Sridhar; Yixin Wang 002; David M. Blei (CoRR, 2021)

Partial Identification with Noisy Covariates: A Robust Optimization Approach. - Wenshuo Guo; Mingzhang Yin; Yixin Wang 002; Michael I. Jordan (CLeaR, 2022)

Empirical Gateaux Derivatives for Causal Inference. - Michael I. Jordan; Yixin Wang 002; Angela Zhou (NeurIPS, 2022)

Breaking Feedback Loops in Recommender Systems with Causal Inference. - Karl Krauth; Yixin Wang 002; Michael I. Jordan (CoRR, 2022)

Valid Inference after Causal Discovery. - Paula Gradu; Tijana Zrnic; Yixin Wang 002; Michael I. Jordan (CoRR, 2022)

A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making. - Linying Zhang; Lauren R. Richter; Yixin Wang 002; Anna Ostropolets; Noemie Elhadad; David M. Blei; George Hripcsak (CoRR, 2022)

Adjusting for indirectly measured confounding using large-scale propensity score. - Linying Zhang; Yixin Wang 002; Martijn J. Schuemie; David M. Blei; George Hripcsak (J. Biomed. Informatics, 2022)

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (Trans. Mach. Learn. Res., 2022)

Identifiable Deep Generative Models via Sparse Decoding. - Gemma E. Moran; Dhanya Sridhar; Yixin Wang 002; David M. Blei (Trans. Mach. Learn. Res., 2022)

Recommendation Systems with Distribution-Free Reliability Guarantees. - Anastasios N. Angelopoulos; Karl Krauth; Stephen Bates; Yixin Wang 002; Michael I. Jordan (COPA, 2023)

Delayed and Indirect Impacts of Link Recommendations. - Han Zhang; Shangen Lu; Yixin Wang 002; Mihaela Curmei (FAccT, 2023)

On Learning Necessary and Sufficient Causal Graphs. - Hengrui Cai; Yixin Wang 002; Michael I. Jordan; Rui Song 006 (NeurIPS, 2023)

CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. - Olivier Jeunen; Thorsten Joachims; Harrie Oosterhuis; Yuta Saito; Flavian Vasile; Yixin Wang 002 (RecSys, 2023)

The Sample Complexity of Online Contract Design. - Banghua Zhu; Stephen Bates; Zhuoran Yang; Yixin Wang 002; Jiantao Jiao; Michael I. Jordan (EC, 2023)

Addressing Hindsight Bias in Multigoal Reinforcement Learning. - Chenjia Bai; Lingxiao Wang 003; Yixin Wang 002; Zhaoran Wang 001; Rui Zhao; Chenyao Bai; Peng Liu 008 (IEEE Trans. Cybern., 2023)

Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. - Yixin Wang 002; Dhanya Sridhar; David M. Blei (Trans. Mach. Learn. Res., 2023)

Learning Equilibria in Matching Markets with Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (J. ACM, 2023)

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification. - Cameron J. Gruich; Varun Madhavan; Yixin Wang 002; Bryan R. Goldsmith (Mach. Learn. Sci. Technol., 2023)

A causality-inspired plus-minus model for player evaluation in team sports. - Caterina De Bacco; Yixin Wang 002; David M. Blei (CLeaR, 2024)

CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. - Olivier Jeunen; Harrie Oosterhuis; Yuta Saito; Flavian Vasile; Yixin Wang 002 (RecSys, 2024)

Causal fairness assessment of treatment allocation with electronic health records. - Linying Zhang; Lauren R. Richter; Yixin Wang 002; Anna Ostropolets; Noémie Elhadad; David M. Blei; George Hripcsak (J. Biomed. Informatics, 2024)

Optimization-based Causal Estimation from Heterogeneous Environments. - Mingzhang Yin; Yixin Wang 002; David M. Blei (J. Mach. Learn. Res., 2024)

Representation Learning: A Causal Perspective. - Yixin Wang 002 (AAAI, 2025)

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference. - Sebastian Salazar; Michal Kucer; Yixin Wang 002; Emily M. Casleton; David M. Blei (AISTATS, 2025)

Bayesian Invariance Modeling of Multi-Environment Data. - Luhuan Wu; Mingzhang Yin; Yixin Wang 002; John P. Cunningham; David M. Blei (CoRR, 2025)