Dr. Tang’s research is in causal inference and its intersection with modern data science. His research interests include causal inference with unmeasured confounding, Mendelian randomization, variable selection for causal modeling, and quantile causal effects. Dr. Tang received his B.S. from the University of Science and Technology of China in 2020, his Ph.D. in Statistics from the University of Toronto in 2024, and worked as a postdoctoral researcher during 2024–2025 at Washington University in St. Louis.
Selected publications
- Tang, D., Kong, D., Pan, W., and Wang, L. (2023) Ultra-high dimensional variable selection for doubly robust causal inference. Biometrics, 79(2), 903-914.
- Zhou,Y., Tang, D., Kong, D., Wang, L. (2024) The Promises of Parallel Outcomes. Biometrika, (2), 537-550.
- Tang, D., Kong, D., and Wang, L. (2025) The synthetic instrument: From sparse association to sparse causation. Journal of the Royal Statistical Society, Series B.