arXiv subjects

Antoine Deeb

Publications and source records attributed to Antoine Deeb.

4 recordsLinked to original sources

arXiv2026

SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary

Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, we study an online resource allocation problem where incoming tasks (cases) must be immediately assigned to available, capacity-constrained resources (mediators). The resources differ in their quality, which may need to be learned. In addition, resources can only be assigned to a subset of tasks that overlaps to varying degrees with the subset of tasks other resources can be assigned to. The objective is to maximize task completion while satisfying soft capacity constraints across all the resources. The scale of the real-world problem poses substantial challenges, since there are over 2000 mediators, and a multitude of combinations of geographic locations (87) and case types (12) that each mediator is qualified to work on. Together, these features-unknown quality of new resources (newly onboarded mediators), soft capacity constraints (due to the mandate to assign cases without delay), and high-dimensional state space-make existing scheduling and resource allocation algorithms either inapplicable or inefficient. We formalize the problem in a tractable manner, using a quadratic program formulation for assignment and a multi-agent bandit style framework for learning. We demonstrate the key properties and advantages of our new algorithm, SMaRT (Selecting Mediators that are Right for the Task), compared with baselines on some stylized instances of the mediator allocation problem. We then turn to considering its application to real-world data on cases and mediators from the Kenyan Judiciary. SMaRT outperforms baselines and allows for controlling the tradeoff between the strictness of the capacity constraints and overall case resolution rates, both in situations where mediator quality is known beforehand and when the problem is bandit-like in that learning is part of the problem definition.

cs.CY↗

arXiv2024

Estimating treatment-effect heterogeneity across sites, in multi-site randomized experiments with few units per site

In multi-site randomized trials with many sites and few randomization units per site, an Empirical-Bayes estimator can be used to estimate the variance of the treatment effect across sites. When this estimator indicates that treatment effects do vary, we propose estimators of the coefficients from regressions of site-level effects on site-level characteristics that are unobserved but can be unbiasedly estimated, such as sites' average outcome without treatment, or site-specific treatment effects on mediator variables. In experiments with imperfect compliance, we show that the sign of the correlation between local average treatment effects (LATEs) and site-level characteristics is identified, and we propose a partly testable assumption under which the variance of LATEs is identified. We use our results to revisit Behaghel et al (2014), who study the effect of counseling programs on job seekers' job-finding rate, in 200 job placement agencies in France. We find considerable treatment-effect heterogeneity, both for intention to treat and LATE effects, and the treatment effect is negatively correlated with sites' job-finding rate without treatment.

econ.EM↗

arXiv2022

Clustering and External Validity in Randomized Controlled Trials

The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In this paper, we consider the case of an RCT with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks. We show that one can draw inference on the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors, or on the ATE netted out of those shocks, using cluster-robust standard errors.

econ.EM↗

arXiv2021

A Framework for Using Value-Added in Regressions

As increasingly popular metrics of worker and institutional quality, estimated value-added (VA) measures are now widely used as dependent or explanatory variables in regressions. For example, VA is used as an explanatory variable when examining the relationship between teacher VA and students' long-run outcomes. Due to the multi-step nature of VA estimation, the standard errors (SEs) researchers routinely use when including VA measures in OLS regressions are incorrect. In this paper, I show that the assumptions underpinning VA models naturally lead to a generalized method of moments (GMM) framework. Using this insight, I construct correct SEs' for regressions that use VA as an explanatory variable and for regressions where VA is the outcome. In addition, I identify the causes of incorrect SEs when using OLS, discuss the need to adjust SEs under different sets of assumptions, and propose a more efficient estimator for using VA as an explanatory variable. Finally, I illustrate my results using data from North Carolina, and show that correcting SEs results in an increase that is larger than the impact of clustering SEs.

econ.EM↗