Robust Causal Inference
Inferring about Treatment Effect with EHR via Drugable Genome
Linking EHR with RCT
Individualized Treatment Effects with Observational Data

Research Team

Cai, Tianxi
Core Director
Expertise: EHR data mining, predictive analytics, clinical trials

Mukherjee, Rajarshi
Assistant Professor of Biostatistics, HSPH
Expertise: causal inference, high dimensional inference

Robins, James

Professor of Epidemiology and Biostatistics
Harvard T.H. Chan School of Public Health

Selected Publications

L Liu, R Mukherjee, JM Robins. “On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning”. arXiv 2020.
S Greenland, MP Fay, EH Brittain, JH Shih, DA Follmann, EE Gabriel, JM Robins. “On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value”. The American Statistician 2019;1–13.
D Cheng, A Chakrabortty, AN Ananthakrishnan, T Cai. “Estimating average treatment effects with a double-index propensity score”. Biometrics 2019.
D Cheng, AN Ananthakrishnan, T Cai. “Robust and efficient semi-supervised estimation of average treatment effects with application to electronic health records data”. Biometrics 2020.
T Cai, T Cai, Z Guo. “Individualized Treatment Selection: An Optimal Hypothesis Testing Approach In High-dimensional Models”. arXiv 2019.
T Cai, Y Zhang, Y Ho, N Link, J Sun, J Huang, TA Cai, S Damrauer, Y Ahuja, J Honerlaw, J Huang, L Costa, P Schubert, C Hong, D Gagnon, YV Sun, JM Gaziano, P Wilson, K Cho, P Tsao, CJ O’Donnell, KP and. “Association of Interleukin 6 Receptor Variant With Cardiovascular Disease Effects of Interleukin 6 Receptor Blocking Therapy”. JAMA Cardiology 2018;3(9):849.
Y Xia, T Cai, TT Cai. “Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions”. Statistica Sinica 2018;63–92.

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