Evan Munro

I am a second-year PhD student in Economics at Stanford GSB, where I work with Guido Imbens and Susan Athey. I am interested in problems at the intersection of machine learning, econometrics, and mechanism design.

Previously, I received an MPhil in Economics from Oxford University and a B.A. in Economics and Computer Science-Mathematics from Columbia University.


Publications and Accepted Papers

Using Wasserstein Generative Adversial Networks for the Design of Monte Carlo Simulations (with Susan Athey, Guido Imbens, and Jonas Metzger)

Accepted, Journal of Econometrics

We discuss using Wasserstein Generative Adversarial Networks (WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom. We apply the methods to compare in three different settings twelve different estimators for average treatment effects under unconfoundedness.

Latent Dirichlet Analysis of Categorical Survey Expectations (with Serena Ng)

Accepted, Journal of Business & Economic Statistics

We propose using a Bayesian hierarchical latent class model to summarize and interpret observed heterogeneity in categorical expectations data. We show that the statistical model corresponds to an economic structural model of information acquisition, which guides interpretation and estimation of the model parameters.

Working Papers

Targeting in Tournaments with Dynamic Incentives (with Martino Banchio)

Status: Submitted

We study the problem of a planner who wants to reduce inequality by awarding prizes to the worst contestants in a tournament without incentivizing shirking. We design an approximately optimal, incentive-compatible mechanism that targets low-ranked contestants based on the tournament's history up to an endogenous stopping time. We describe applications to eligibility for remedial education, retraining benefits for the unemployed, and draft lotteries in sports.

Work in Progress

Experimental Design of Robust Predictive Policy

Most existing methods to estimate the optimal prediction function when agents are strategic require full knowledge of the environment, including distributional and functional form assumptions. I design a dynamic experiment that is simple to implement and recovers an optimal prediction function under a general class of strategic behavior, without knowledge of the structure.