My CV is available here.

Working papers

Experimental design for policy choice (pdf)

I design experiments that directly inform better policy decisions in the face of constraints.

Abstract I study how to design experiments for the objective of choosing optimal policies. An experimenter wants to choose a policy to maximize welfare subject to budget or other policy constraints. The effects of counterfactual policies are described by a structural econometric model governed by an unknown parameter. The experimenter has access to some pilot data, and has the opportunity to collect additional data through an experiment. The joint experimental design and policy choice problem is a dynamic optimization problem with a very high-dimensional state space, since the chosen policy depends on the realized data. I propose a low-dimensional approximation to the solution and show it is asymptotically optimal under Bayes expected welfare. The method applies to policies allocating discrete as well as continuous treatments, such as cash transfers, prices, or tax credits, and also allows targeting the policy based on covariates. I demonstrate the method using the conditional cash transfer program Progresa, showing how to design an experiment to help choose a policy aimed at increasing graduation rates and reducing gender disparities in education. Compared to the original Progresa experiment, the optimal experiment requires only one quarter as many observations to obtain equally effective policies.

Work in progress

Distributionally robust optimal transport for program evaluation
(with Omkar A. Katta and Guillaume Pouliot)

We use distributionally robust optimal transport to conduct inference on the distribution of treatment effects with covariates.

Abstract Many partially identified parameters in program evaluation settings are instances of the general Fréchet problem of bounding a functional of a joint distribution when only its marginals are observed. A leading example is the distribution of treatment effects. Using data on covariates can tighten the identified set, but doing so nonparametrically is difficult in practice. We propose a distributionally robust optimal transport framework for inference on the solution to the Fréchet problem which nonparametrically incorporates covariate data and show it delivers valid inference on these parameters. We show our infinite-dimensional distributionally robust optimal transport problem has a finite-dimensional linear programming formulation, facilitating computation.

Published and accepted papers

Policy learning with new treatments (pdf)

Quantitative Economics, 16.4 (2025): 1409-1456

I estimate optimal policies when experiments cover only a subset of possible treatments, using minimax regret.

Abstract I study the problem of a decision maker choosing a policy that allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear‐ and integer‐programming formulation. I prove that the rate at which the maximum regret of the estimated policy converges to the lowest possible maximum regret is the maximum of N −1/2 and the rate at which conditional average treatment effects are estimated in the experimental data. In an application to designing targeted subsidies for electrical grid connections in rural Kenya, I find that nearly the entire population should be given a treatment not implemented in the experiment, reducing maximum regret by over 60% compared to the policy that restricts to the treatments implemented in the experiment.

Mount Timpanogos from Kyhv Peak. Credit: Emma Higbee