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Research by Area

Papers organized by research theme. For a chronological listing, see Methodological Work and Review, Workshop Publication, and Applied Work on the home page.

Causal Discovery

Cross-theme connections

  • MixCIT: A Kernel Based Local-Polynomial Debiased Test for Conditional Independence on Mixed-Type Data with Mengxiao Gao, Promit Ghosal (arXiv)
    CI / hypothesis testing; primary use case is causal discovery.

  • From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? with Sujai Hiremath, Dominik Janzing, Philipp Faller, Patrick Blobaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan (arXiv) (AISTATS 2026)

  • When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery with Dominik Meier, Sujai Hiremath, Promit Ghosal (arXiv) (NeurIPS 2025)

  • LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery with Sujai Hiremath, Promit Ghosal (arXiv) (UAI 2025)

  • Local Causal Discovery for Structural Evidence of Direct Discrimination with Jacqueline Maasch, Violet Chen, Agni Orfanoudaki, Nil-Jana Akpinar, Fei Wang (arXiv) (AAAI 2025)
    LD3 returns a set optimal for weighted controlled (direct) effect. Related: Optimal Adjustment Sets

  • Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models with Sujai Hiremath, Jaqueline Maasch, Mengxiao Gao, Promit Ghosal (arXiv) (NeurIPS 2024)

  • Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs with Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Fei Wang (arXiv) (UAI 2024) (NeurIPS Causal Representation Learning Workshop 2023)
    LDP — local discovery aimed at causal inference (with LD3)

Causal, Longitudinal & Sequential Inference

Cross-theme connections

Longitudinal

  • Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation with Wenxin Chen, Weishen Pan, Fei Wang (arXiv) (KDD 2026)

  • Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings with Wenxin Chen, Weishen Pan, Fei Wang (arXiv) (ICML 2026)

Static

  • MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability with Wenxin Chen, Weishen Pan, Fei Wang (arXiv) (ICML 2026)

  • Targeted Maximum Likelihood Learning: An Optimization Perspective with Diyang Li (NeurIPS 2025) (a copy here)

  • Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect with Ruiyang Lin, Yongyi Guo (arXiv) (NeurIPS 2025)
    LD3 returns a set optimal for this WCDE estimand. Related: LD3

  • Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters with Brian Cho, Yaroslav Mukhin, and Ivana Malenica (arXiv) (ICML 2024)
    Finalist, 2023 INFORMS DMDA Workshop Best Paper Competition -- Theoretical Track

  • Causal Inference with Selectively Deconfounded Data with Andrew Li, Zachary Lipton, and Sridhar Tayur (AISTATS 2021) (Journal version) (NeurIPS CausalML Workshop 2019)

  • Federated Causal Inference in Healthcare: Methods, Challenges, and Application with Haoyang Li, Jie Xu, Fei Wang, Chengxi Zang (arXiv)

Sequential

  • Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits with Brian Cho, Dominik Meier, Nathan Kallus (arXiv) (AISTATS 2025)

  • CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies with Brian Cho, Ana-Roxana Pop, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus (arXiv) (KDD 2025)

  • Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams with Brian Cho and Nathan Kallus (arXiv) (ICML 2024)

  • Anytime-Valid Inference in N-of-1 Trials with Ivana Malenica, Yongyi Guo, and Stefan Konigorski (arXiv) (ML4H 2023)

Adaptive Experimentation and Reinforcement Learning

Cross-theme connections

  • Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure with Jiamin Xu, Jacqueline Maasch (arXiv)
    Bridges causal structure / discovery with deep RL.

  • Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding with Jiamin Xu (arXiv) (ICML 2026)

  • From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance with Jiamin Xu, Ivan Nazarov, Aditya Rastogi, Africa Perianez (arXiv) (AISTATS 2026)

  • Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health with Xueqing Liu, Esmaeil Keyvanshokooh, and Susan A. Murphy (arXiv)

  • Learning When to Nudge: A Dual-Agent Bandit Framework for Behavioral Interventions with Esmaeil Keyvanshokooh, Yongyi Guo, Xueqing Liu, and Susan A. Murphy (Journal version)

  • Contextual Bandits with Budgeted Information Reveal with Esmaeil Keyvanshokooh, Xueqing Liu, and Susan A. Murphy (arXiv) (AISTATS 2024)

  • Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing with Su Jia, Andrew Li, and Sridhar Tayur (SSRN) (Management Science, 2025)

  • Greedy Approximation Algorithms for Active Sequential Hypothesis Testing with Su Jia, and Andrew Li (NeurIPS 2021)
    Winner, 2021 INFORMS Pierskalla Best Paper Award

  • Optimizing Wearable Devices in Personalized Opioid Use Disorder Treatments Under Budget Constraint with Yanhan (Savannah) Tang, Alan Scheller-Wolf and Sridhar Tayur (Journal version)
    Canadian Healthcare Optimization Workshop (CHOW) best paper in the category of operations research/management science, 2021
    Finalist, 2019 INFORMS IBM Service Science Best Student Paper Award

Generative Models

  • Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces with Shixing Yu, Promit Ghosal (arXiv)

  • Clustering by Denoising: Latent Plug-and-Play Diffusion for Single-Cell Data with Dominik Meier, Shixing Yu, Sagnik Nandy, Promit Ghosal (arXiv) (ICLR 2026)

Applications