Interests

  • Experimental design
  • Treatment choice and policy design under uncertainty
  • Nonparametric estimation and inference via optimal transport

Working Papers

Policy Learning with New Treatments (pdf)

Revision requested at Quantitative Economics

I study the problem of a decision maker choosing a policy to allocate 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 based on shape restrictions on treatment response. I propose solving an empirical minimax regret problem to estimate the policy and show it has a tractable linear- and integer-programming formulation. I prove the maximum regret of the estimator converges to the lowest possible maximum regret at the rate at which heterogeneous treatment effects can be estimated in the experimental data or N^(-1/2), whichever is slower. I apply my results to design targeted subsidies for electrical grid connections in rural Kenya, and estimate that 97% of the population should be given a treatment not implemented in the experiment.