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20242026
most citedEstimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory

12 citations · 27 across the 6 of their papers we have counts for

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cs.CL2024★ 2 cited

Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models

Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi +3

Language models (LMs) are increasingly used to simulate human-like responses in scenarios where accurately mimicking a population's behavior can guide decision-making, such as in d…

stat.ME2024★ 4 cited

Polytomous Explanatory Item Response Models for Item Discrimination: Assessing Negative-Framing Effects in Social-Emotional Learning Surveys

Joshua B. Gilbert, Lijin Zhang, Esther Ulitzsch +1

Modeling item parameters as a function of item characteristics has a long history but has generally focused on models for item location. Explanatory item response models for item d…

econ.EM2024★ 12 cited

Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory

Joshua B. Gilbert, Zachary Himmelsbach, James Soland +2

Analyses of heterogeneous treatment effects (HTE) are common in applied causal inference research. However, when outcomes are latent variables assessed via psychometric instruments…

stat.ME2024★ 9 cited

Item-Level Heterogeneous Treatment Effects of Selective Serotonin Reuptake Inhibitors (SSRIs) on Depression: Implications for Inference, Generalizability, and Identification

Joshua B. Gilbert, Fredrik Hieronymus, Elias Eriksson +1

In analysis of randomized controlled trials (RCTs) with patient-reported outcome measures (PROMs), Item Response Theory (IRT) models that allow for heterogeneity in the treatment e…