My primary area of training is in the use of Bayesian methods (e.g., Gaussian Processes) for applications in Causal Inference and Experimental Design. Since 2019, I have focused on projects in the information technology domain. A list of select papers is given below:
Friedberg, R., Zaidi, A., Mudd, R., Johnstone, P., et al. (2025). Treatment Effect Learning Under Sequential Randomization. arXiv. https://doi.org/10.48550/arxiv.2510.20078
Mudd, R., Zaidi, A., Friedberg, R., Gorbachev, I., Choubey, A., & Nassif, H. (2026). Breaking the Winner's Curse with Bayesian Hybrid Shrinkage. arXiv. https://doi.org/10.48550/arxiv.2603.12867
Xue, W., & Zaidi, A. (2021). Bayesian Sensitivity Analysis for Missing Data Using the E-value. arXiv. https://doi.org/10.48550/arxiv.2108.13286
Zaidi, A., Friedberg, R., Khan, S., Leow, Y.-Y., Soneji, M., Nassif, H., & Mudd, R. (2025). Bayesian Predictive Probabilities for Online Experimentation. arXiv. https://doi.org/10.48550/arxiv.2511.06320
Zaidi, A., & Mukherjee, S. (2018). Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects. arXiv. https://doi.org/10.48550/arxiv.1812.07153