My CV is available here.

Working papers

Experimental design for policy choice (pdf) (arXiv)

New version!

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

Abstract We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem involves a dynamic program with a very high-dimensional state and is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

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) (arXiv) (published version)

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