Kan Chen

Kan Chen

Postdoctoral Research Fellow at Harvard and Columbia/PhD from UPenn

Preprint and Peer-reviewed Publications by Thematic Area

1. Causal Inference

This body of work advances statistical methodologies for causal inference using observational data, with a focus on partial identification, sensitivity analysis, mediation analysis, and case-control designs to address challenges such as unmeasured confounding bias, selection bias, and residual bias. These methods are applied to real-world problems in social policy, clinical research, and oncology studies.

2. Causal Machine Learning and AI

This research develops and applies machine learning methods for treatment effect estimation, including interpretable deep learning models, privacy-preserving algorithms, and large-scale emulated trials. It spans a broad range of topics, encompassing optimization theory for high-dimensional statistics and overparameterized neural networks, with the goal of integrating modern machine learning tools into rigorous and scalable causal analysis frameworks.

3. Scientific Collaborations

These collaborative studies apply rigorous statistical and machine learning techniques to pressing biomedical and health policy questions. Topics include patient-centric research study, clinical trial design, palliative care, and the integration of patient-reported outcomes in predictive modeling.

Preprint

  1. Kan Chen, Ruoyu Wang, Zhonghua Liu, Xihong Lin. (2025) “Mitigating Unmeasured Confounding Bias in Large-Scale Causal Mediation Analysis Via Factor Analysis in Epigenome-Wide Studies.” Journal of the American Statistical Association. Submitted. Link.
  2. Kan Chen, Ting Ye, Greg Ridgeway, Dylan S. Small. (2025) “Evaluating ‘Stand Your Ground’ Laws: A Bracketing Approach for Partial Identification in Difference-in-Difference Analysis with Staggered Adoption.” Annals of Applied Statistics. Submitted. Link.
  3. Kan Chen, Inyoung Choi, Ravi Parikh, Qi Long. (2025) “CROME: Covariate-Balanced Causal Representation Learning for Composite Outcomes via Multi-Task Estimation in Electronic Health Records.” ICLR 2026. Submitted. Link.
  4. Kan Chen, Jeffery Zhang, Bingkai Wang, Dylan S. Small. (2023) “A Differential Effect Approach to Partial Identification of Treatment Effects.” Biometrika, major revision. https://arxiv.org/abs/2303.06332
    (Recipient of the 2024 IMS New Researcher Travel Award)

Peer-reviewed Publications

Causal Inference

  1. Kan Chen, Ting Ye, Dylan Small. (2025) “Sensitivity Analysis for Attributable Effects in Case² Studies.” Biometrics 81 (3), ujaf102. https://doi.org/10.1093/biomtc/ujaf102
  2. Kan Chen, Jing Cheng, M. Elizabeth Halloran, Dylan S. Small. (2025) “Using Case Description Information to Reduce Sensitivity to Bias for the Attributable Fraction Among the Exposed.” Journal of the Royal Statistical Society: Series A, qnaf066. https://doi.org/10.1093/jrsssa/qnaf066
    (Recipient of the 2023 ASA Statistics in Epidemiology Young Investigator Award)
  3. Ting Ye, Kan Chen, Dylan S. Small. (2025) “Combining Broad and Narrow Case Definitions in Matched Case-Control Studies: Firearms in the Home and Suicide Risk.” Journal of the American Statistical Association, 120(550), 698–709. https://doi.org/10.1080/01621459.2024.2441519
  4. Kan Chen, Siyu Heng, Qi Long, Bo Zhang. (2023) “Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies.” Journal of Computational and Graphical Statistics, 32(2), 528–538. https://doi.org/10.1080/10618600.2022.2116447

Causal Machine Learning and AI

  1. Orcutt Xavier, Kan Chen, Ronac Mamtani, Qi Long, Ravi Parikh. (2025) “Evaluating Generalizability of Results from Landmark Randomized Controlled Trials in Oncology Using Machine Learning-Based Emulated Trials.” Nature Medicine, 31(2), 457–465. https://doi.org/10.1038/s41591-024-03352-5
  2. Yinjun Wu, Neelay Velingker, Kan Chen, et al. (2024) “DISCRET: Synthesizing Faithful Explanations for Treatment Effect Estimation.” Proceedings of the 41st International Conference on Machine Learning (ICML 2024) 235 (2024): 53597. https://openreview.net/forum?id=B0xmynxt4f
    (Spotlight presentation, acceptance rate 3.5%)
  3. Kan Chen, Qishuo Yin, Qi Long. (2023) “Covariate-Balancing-Aware Interpretable Deep Learning Models for Treatment Effect Estimation.” Statistics in Biosciences, 17(1), 132–150. https://doi.org/10.1007/s12561-023-09394-6
    (Recipient of the 2022 ICSA Jiann-Ping Hsu Student Paper Award and NSF Travel Award)
  4. Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long. (2022) “Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 604–619. https://link.springer.com/chapter/10.1007/978-3-031-26412-2_37
  5. Kan Chen, Shiyun Xu, Zhiqi Bu. (2021) “Asymptotic Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 510–526. https://doi.org/10.1007/978-3-030-86523-8_31
  6. Zhiqi Bu, Shiyun Xu, Kan Chen. (2021) “A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks.” International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 3187–3195. https://proceedings.mlr.press/v130/bu21a.html
  7. Kan Chen, Qi Long. (2021) “Distributed Gaussian Differential Privacy Via Shuffling.” ICLR 2021 Workshop on Distributed and Private Machine Learning. https://dp-ml.github.io/2021-workshop-ICLR/files/2.pdf

Scientific Collaboration and Peer-Reviewed Abstracts

  1. Sophia Shi, Kan Chen, Qi Long, Ravi Parikh. (2025) “Effect of Incorporating Symptom Burden With Mortality as a Composite Outcome on Accuracy and Bias in Palliative Care Identification Algorithms in Oncology.” Journal of Clinical Oncology 43(16_suppl), Abstract 12016. https://doi.org/10.1200/JCO.2025.43.16_suppl.12016
  2. Christopher R. Manz, Yichen Zhang, Kan Chen, et al. (2023) “Machine Learning-Triggered Behavioral Nudges on Serious Illness Communication and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial.” JAMA Oncology, 9(4), 414–418. https://doi.org/10.1001/jamaoncol.2022.6303
    (Selected Best Papers for the 2024 IMIA Yearbook)
  3. Yehoda M. Martei, Kan Chen, Ronac Mamtani, Lawrence N. Shulman, Rebecca A. Hubbard. (2023) “Racial disparities in utilization of first-line targeted therapies for metastatic breast cancer.” Journal of Clinical Oncology 41(16_suppl), Abstract 6528. https://doi.org/10.1200/JCO.2023.41.16_suppl.6528
  4. Mia Djulbegovic, Meredith Doherty, Katie Fanslau, Kan Chen, et al. (2023) “A Randomized Controlled Trial of a Financial Navigation Program for Patients with Multiple Myeloma.” Blood 142(Suppl 1): Abstract 909. https://doi.org/10.1182/blood-2023-174451
  5. Stephen J. Bagley, Divij Mathew, Kan Chen, et al. (2023) “PD1 Inhibition and GITR Agonism in Combination With Fractionated Stereotactic Radiotherapy for the Treatment of Recurrent Glioblastoma: A Phase 2, Multi-Arm Study.” Journal of Clinical Oncology 41(16_suppl), Abstract 2004. https://doi.org/10.1200/JCO.2023.41.16_suppl.2004
  6. Stephen Bagley, Jacob Shabason, Kan Chen, et al. (2022) “CTIM-35. A Phase II Study of GITR Agonist INCAGN01876 and PD-1 Inhibitor Retifanlimab in Combination With Stereotactic Radiotherapy in Patients With Recurrent Glioblastoma.” Neuro-Oncology 24(Suppl 7): Abstract vii69. https://doi.org/10.1093/neuonc/noac209.267