I am an econometrician engaging in both theoretical and applied work. My research spans two main areas: causal inference, including experiment designs and policy learning, and nonparametric estimation.

In my job market paper, I examine how adaptive experiments can enhance the estimation of a variety of causal parameters, aiming to inform policy decisions that account for more complex objectives beyond average effects.

I expect to graduate in Spring 2026 and will be available for interviews in the 2025-2026 job market.

Job Market Paper

Adaptive Experiment Design for Estimating a General Class of Causal Effects

Abstract: This paper develops a unified adaptive experiment framework that targets efficient estimation of a general class of causal parameters. Beyond average and quantile treatment effects, the framework accommodates distributional effects, inequality measures, and other policy-relevant targets. Since experiments often inform real world decisions with more nuanced objectives, this adaptive approach broadens the scope of experimentation for practical decision-making. In this framework, treatment randomization is updated sequentially based on accumulated data, enabling estimators to achieve minimal asymptotic variance. Theoretical results demonstrating optimal efficiency are supported by empirical illustrations based on data from the Oregon Health Experiment and other simulation evidence.

Working Papers

Policy Learning with Compliance Guarantee (joint with Vadim Marmer & Kyungchul Song)

Abstract: We study optimal policy learning where a policy maker uses policy outcome data from a source population to design treatment assignments for a target population under budget constraint. Due to the budget constraint, the policy maker needs to consider both the treatment effects and individuals’ incentives for treatment participation to minimize wasted resources. The main challenge is that treatment participation incentives may differ between the two populations. We develop a maximin approach that maximizes the minimum expected treatment outcome across all possible incentive configurations. We find that this optimal policy learning problem transforms into one with stochastic dominance constraints, where optimal assignment prioritizes individuals most likely to comply with the treatment assignment.

Work in Progress

Asymmetric and Optimal Bandwidth Selection in Estimation for First Price Auctions

Abstract: I analyze bandwidth selection in the estimator proposed by Guerre, Perrigne, and Vuong (2000). I extend the inference framework of Ma, Marmer, and Shneyerov (2019) to cases where the ratio of the first- to second-stage bandwidths converges to either zero or infinity. In such regimes, the asymptotic normality is governed by the stage with the slower bandwidth rate. Further analysis shows that minimizing the pointwise mean squared error requires the bandwidth ratio to converge to zero. This result is driven by a bias-variance tradeoff that arises across the two estimation stages under certain conditions.

• Faculty of Arts Graduate Award
• Bank of Montreal Graduate Fellowship