Research

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 \textit{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.s.

We propose a novel randomization approach for randomized controlled trials (RCTs), named the cube method. The cube method allows for the selection of balanced samples across various covariate types, ensuring consistent adherence to balance tests and, whence, substantial precision gains when estimating treatment effects. We establish several statistical properties for the population and sample average treatment effects (PATE and SATE, respectively) under randomization using the cube method. The relevance of the cube method is particularly striking when comparing the behavior of prevailing methods employed for treatment allocation when the number of covariates to balance is increasing. We formally derive and compare bounds of balancing adjustments depending on the number of units n and the number of covariates p and show that our randomization approach outperforms methods proposed in the literature when p is large and p/n tends to 0. We run simulation studies to illustrate the substantial gains from the cube method for a large set of covariates

This paper presents the first rigorous evaluation of school-based interventions aimed at reducing LGBTphobia. We focus on a classroom intervention that addresses the issue of LGBT harassment through perspective-taking and narrative exchange. Using a field experiment in France with more than 10,000 middle and high school students, we find robust evidence of strong positive effects, with variations across gender, age, and socio-economic status. We argue that changing perceptions of group norms is a key channel driving these heterogeneous effects.