Pedro Vergara Merino – Job Market Candidate 2026

Personal website:

https://sites.google.com/view/pedrovergaramerino/home?authuser=0

Contact:

pedro.vergaramerino@ensae.fr

References:

  • Pr. Laurent Davezies – CREST, Groupe ENSAE-ENSAI, Institut Polytechnique de Paris
  • Pr. Guillaume Hollard – CREST, CNRS, Institut Polytechnique de Paris
  • Pr. Max Tabord-Meehan – University of Toronto

Research fields:

Primary fields: Econometrics
Secondary fields: Applied Microeconomics

Presentation: 

I am a PhD student in Economics at CREST-ENSAE Paris, under the supervision of Laurent Davezies and Guillaume Hollard.
For the Winter and Spring trimesters 2025, I visited the University of Chicago.
My research focuses on experimental design, causal inference, and applied microeconomics, with particular interests in discrimination and LGBTQ+ economics.

Job Market Paper:

Waiting for Balance: Covariate-Adaptive Randomization in Sequential Experiments
Abstract: When assigning units to treatment and control, researchers are often confronted with the sequential arrival of participants over time (e.g., job seekers, patients). The challenge in such settings is to assign participants sequentially while maintaining covariate balance between treatment arms. This paper introduces the sequential cube method (SCM), a new design that achieves near-exact balance in covariate moments at the cost of only a short waiting period before treatment assignment. I first show that exact balance, for a given function of covariates, delivers the optimal precision of treatment effect estimators. Under general conditions, I prove that SCM attains near-exact balance. Moreover, I establish that the expected waiting time under SCM grows only in proportion to the number of covariates used for balancing, making the procedure scalable in practice. I further derive the asymptotic normality of average treatment effect estimators under SCM, ensuring valid inference. Simulation studies and empirical applications highlight the practical advantages of SCM. Relative to alternative balancing designs, SCM (i) improves covariate balance, (ii) increases the precision of treatment effect estimators, and (iii) requires substantially shorter waiting times. Finally, I discuss extensions to multiple treatments and response-adaptive randomization, encompassing multi-armed bandit settings.