Research

Publications

• Disentangling person-dependent and item-dependent causal effects: Applications of item response theory to the estimation of treatment effect heterogeneity with Joshua Gilbert, Luke Miratrix, and Ben Domingue, Journal of Educational and Behavioral Statistics, 2024 [Working paper]
  Show summary

  This paper shows that identical patterns of treatment effect heterogeneity can arise either from differences in student ability or from how specific test items respond to an intervention. Using item response theory within a causal framework, we demonstrate that these sources of variation are indistinguishable from total scores alone. We introduce a model that uses item-level data to separate person- and item-dependent effects and apply it to a reading intervention to illustrate how this distinction matters for interpretation.

Working Papers

• Hard to read: The impact of advanced reading assignments on language and literacy outcomes in Nigeria (with Colin Aitken, Guthrie Gray-Lobe, Michael Kremer, Joost de Laat, and Wendy Wong)
  Show summary

  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.

• Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory (with Joshua Gilbert, Zachary Himmelsbach, James Soland, and Benjamin Domingue) [Working paper]
  Show summary

  This paper introduces a framework for estimating heterogeneous treatment effects when the outcome is measured using a psychometric scale—like a test or survey—by leveraging item response theory to detect variation at the item level. Standard approaches ignore the possibility that different items may respond differently to treatment, which can distort findings and underestimate uncertainty. By analyzing over 5 million item responses across 75 datasets, we show that modeling “item-level” heterogeneity not only yields more accurate standard errors and treatment effect estimates but also resolves key identification problems and improves generalizability to new settings.

In Progress

• Evaluating the effectiveness of a digital empowerment curriculum for college students in India (with Jalnidh Kaur and Lena Song) Conditionally accepted at the Journal of Development Economics based on pre-results review
  Show summary

  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.

• Active learning and undergraduate STEM student achievement: Re-examining evidence in light of novel, large-scale natural experiments (with Prashant Loyalka, Saurabh Khanna, and Paul Glewwe)
  Show summary

  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.

• A STEM Instructor Like Me: Female Teacher-Student Interactions in Engineering Colleges (with Rob Fairlie, Saurabh Khanna, Prashant Loyalka, and Gagandeep Sachdeva)
  Show summary

  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.

Resting Papers

• Estimating the effect of peers on non-cognitive skills of adolescents using friendship networks