Mridul Joshi

This study evaluates a digital empowerment intervention designed to improve how college students in India engage with smartphones and social media. Using a randomized controlled trial, we test both a high-touch classroom course and a low-touch text message version. The intervention addresses self-control problems and habit formation, introduces commitment devices, and builds skills for identifying misinformation. We combine survey data, administrative records, and high-frequency smartphone tracking to estimate effects on mental health, academic outcomes, and digital behavior.

This study examines how faculty gender influences both academic and psychological outcomes for STEM students. Leveraging a rare setting in which engineering students in India are randomly assigned to instructors, we estimate the causal effects of exposure to female faculty across multiple undergraduate institutions. The design combines administrative data, standardized assessments, and surveys measuring STEM-related confidence, anxiety, and beliefs about gender and ability—offering new evidence on how representation and identity shape student experiences in higher education.

This study examines the impact of active learning on STEM achievement through a systematic review and new empirical analyses. We assess the methodological quality of existing research and combine this with original data from a large-scale student–instructor random assignment in India and nationally representative samples from China, India, and Russia. Anchored in competing theoretical perspectives from cognitive science, the study investigates when, how, and for whom active learning may, or may not, impact learning outcomes in higher education.

This study tests whether assigning more advanced reading material to stronger third-grade students can improve literacy outcomes in low-cost private schools in Nigeria. In a randomized trial across 60 schools, the intervention modified a structured, tablet-based reading program to better align reading difficulty with student ability. The study combines experimental results with a formal model of learning that incorporates non-convex losses from mistargeted instruction—where learning declines sharply once material exceeds a student’s zone of proximal development. This approach challenges standard assumptions in instructional targeting and offers new insight into the limits of personalized learning at scale.

We evaluate the scale up of Morocco’s Pioneer School Program beyond its initial implementation, examining whether the large learning gains observed in the first year persist with longer exposure and extend to newer cohorts. The program, which combines structured pedagogy and targeted remediation, expanded from 626 schools in 2023–24 to 4,626 schools in 2025–26. We use a difference-in-differences design and student assessments in Arabic, French, and mathematics to estimate longer term treatment effects.

Analyses of heterogeneous treatment effects (HTE) are common in applied causal inference research. However, when outcomes are latent variables assessed via psychometric instruments such as educational tests, standard methods ignore the potential HTE that may exist among the individual items of the outcome measure. Failing to account for “item-level” HTE (IL-HTE) can lead to both underestimated standard errors and identification challenges in the estimation of treatment-by-covariate interaction effects. We demonstrate how Item Response Theory (IRT) models that estimate a treatment effect for each assessment item can both address these challenges and provide new insights into HTE generally. This study articulates the theoretical rationale for the IL-HTE model and demonstrates its practical value using 75 datasets from 48 randomized controlled trials containing 5.8 million item responses in economics, education, and health research. Our results show that the IL-HTE model reveals item-level variation masked by single-number scores, provides more meaningful standard errors in many settings, allows for estimates of the generalizability of causal effects to untested items, resolves identification problems in the estimation of interaction effects, and provides estimates of standardized treatment effect sizes corrected for attenuation due to measurement error.

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment. This study demonstrates that the identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a preintervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction.