Chakrabortty, Abhishek Assistant Professor

academicarticle

• Zhang, Y., Chakrabortty, A., & Bradic, J. (2023). The Decaying Missing-at-Random Framework: Doubly Robust Causal Inference with Partially Labeled Data
  
• Zhang, Y., Chakrabortty, A., & Bradic, J. (2023). Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap. 12(3), 1-94.
  
• Kuchibhotla, A. K., & Chakrabortty, A. (2022). Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression. Information and Inference. 
  
• Chakrabortty, A., Dai, G., & Tchetgen, E. T. (2022). A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
  
• Chakrabortty, A., Dai, G., & Carroll, R. J. (2022). Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings
  
• Cheng, D., Chakrabortty, A., Ananthakrishnan, A. N., & Cai, T. (2020). Estimating Average Treatment Effects with a Double-Index Propensity Score. Biometrics.  76(3), 767-777.
  
• Chan, S. F., Hejblum, B. P., Chakrabortty, A., & Cai, T. (2020). Semi-supervised estimation of covariance with application to phenome-wide association studies with electronic medical records data. Statistical Methods in Medical Research.  29(2), 455-465.
  
• Chakrabortty, A., Lu, J., Cai, T. T., & Li, H. (2019). High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
  
• Chakrabortty, A., Nandy, P., & Li, H. (2018). Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
  
• Chakrabortty, A., & Cai, T. (2018). EFFICIENT AND ADAPTIVE LINEAR REGRESSION IN SEMI-SUPERVISED SETTINGS. ANNALS OF STATISTICS.  46(4), 1541-1572.
  
• Yu, S., Chakrabortty, A., Liao, K. P., Cai, T., Ananthakrishnan, A. N., Gainer, V. S., ... Cai, T. (2017). Surrogate-assisted feature extraction for high-throughput phenotyping. Journal of the American Medical Informatics Association : JAMIA.  24(e1), e143-e149.
  
• Chakrabortty, A., Neykov, M., Carroll, R., & Cai, T. (2017). Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes
  
• Ghosh, S., & Chakrabortty, A. (2014). A Poisson regression model for association mapping of count phenotypes. Molecular Cytogenetics.  7(Suppl 1), o1-O1.
  
• Roy, S., Kumaravel, S., Sharma, A., Duran, C. L., Bayless, K. J., & Chakraborty, S. Hypoxic tumor microenvironment: Implications for cancer therapy. Experimental Biology and Medicine.  245(13), 1073-1086.