My research focuses on problems in causal inference, statistical learning, and optimization. I am currently working on causal inference in dynamic systems and causal inference under distribution shift.
Non-parametric Causal Inference in Dynamic Thresholding Designs
Aditya Ghosh and Stefan Wager
Submitted
· 2025+
We study thresholding designs in general dynamic systems, and show that simple reduced-form characterizations remain available for a relevant causal target, namely a dynamic marginal policy effect at the treatment threshold.
Which Covariates to Adjust for? Specification-robust Causal Inference in Observational Studies
Aditya Ghosh and Dominik Rothenhäusler
Submitted
· 2025+
When it is unclear which covariates to adjust for, we provide valid inference for a reweighted population when at least one of the candidate adjustment sets is valid.
PLRD: Partially Linear Regression Discontinuity Inference
Aditya Ghosh, Guido Imbens, and Stefan Wager
Submitted
· 2025+
For treatment rules with an eligibility threshold, PLRD delivers narrow yet reliable confidence intervals for the effect at the threshold by solving a minimax problem.
Robustness and Efficiency of Rosenbaum's Rank-based Estimator in Randomized Trials: A Design-based Perspective
Aditya Ghosh, Nabarun Deb, Bikram Karmakar, and Bodhisattva Sen
Accepted at Biometrika
· 2026
We study efficiency and robustness of Rosenbaum's rank-based estimator under finite population inference in randomized trials, with and without covariate adjustment.
An Asymptotic Formula for the Chebyshev Theta Function
Aditya Ghosh
Notes on Number Theory and Discrete Mathematics, 25(4), 1–7
· 2019
We derive sharp bounds on the geometric mean of the first n prime numbers.