Real World Evidence

• Robust Causal Inference

• Inferring about Treatment Effect with EHR via Drugable Genome

• Linking EHR with RCT

• Individualized Treatment Effects with Observationa Data

Research Team

Cai, Tianxi

Mukherjee, Rajarshi

Robins, James

Selected Publications

• L Liu, R Mukherjee, JM Robins, 2019, “On assumption-free tests and confidence intervals for causal effects estimated by machine learning”, https://arxiv.org/abs/1904.04276.

• Greenland, S., Fay, M.P., Brittain, E.H., Shih, J.H., Follmann, D.A., Gabriel, E.E. and Robins, J.M., 2019. On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value. The American Statistician, pp.1-11.

• D. Cheng, A. Ananthakrishnan, and T. Cai, 2019 “Estimating Average Treatment Effects with a Double-Index Propensity Score.” Biometrics, in revision.

• D. Cheng, A. Ananthakrishnan, and T. Cai, 2019 “Efficient and Robust Semi-Supervised Estimation of Average Treatment Effects in Electronic Medical Records Data.” Biometrics, in revision.

• T. Cai, T.T. Cai, Z. Guo, 2019 “Individualized Treatment Selection: An Optimal Hypothesis Testing Approach In High-dimensional Models”. https://arxiv.org/abs/1904.12891.

• T. Cai, Y. Zhang, Y. Ho, N. Link, J. Sun, J. Huang, T.A. Cai, S. Damrauer, Y. Ahuja, et al., 2018. “Association of interleukin 6 receptor variant with cardiovascular disease effects of interleukin 6 receptor blocking therapy: a phenome-wide association study.” JAMA cardiology, 3(9), pp.849-857.

• Y. Xia, T. Cai, T.T. Cai, 2018 “Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions”. Statistica Sinica, 28(1), 63-92.