Joshua B. Gilbert, Zachary Himmelsbach, James Soland, Mridul Joshi, Benjamin W. Domingue
arXiv 30 Apr 2024 · Econometrics · publishedJournal of Policy Analysis and Management (2025) · 10 citations (OpenAlex)
arXiv:2405.00161 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gilbert, Joshua B (2024) How measurement affects causal inference: Attenuation bias is (usually) more important than scoring weights self | 1.000 | 7 | 3 | 100% |
| 2 | Gilbert, Joshua B, Kim, James S, Miratrix, Luke W (2023) Modeling item-level heterogeneous treatment effects with the explanatory item response model: Leveraging large-scale online asse… self | 0.980 | 17 | 5 | 94% |
| 3 | Gilbert, Joshua B, Miratrix, Luke W, Joshi, Mridul, Domingue, Benjam… (2025) Disentangling person-dependent and item-dependent causal effects: Applications of item response theory to the estimation of trea… self | 0.965 | 10 | 4 | 90% |
| 4 | Gilbert, Joshua B, Kim, James S, Miratrix, Luke W (2024) Leveraging item parameter drift to assess transfer effects in vocabulary learning self | 0.961 | 9 | 5 | 89% |
| 5 | Gilbert, Joshua B., Hieronymus, Fredrik, Eriksson, Elias, Domingue,… (2024) Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for in… self | 0.953 | 15 | 5 | 87% |
| 6 | Kim, James S, Relyea, Jackie Eunjung, Burkhauser, Mary A, Scherer, E… (2021) Improving elementary grade students’ science and social studies vocabulary knowledge depth, reading comprehension, and argumenta… | 0.941 | 6 | 3 | 83% |
| 7 | Gilbert, Joshua B (2023) Estimating treatment effects with the explanatory item response model self | 0.928 | 4 | 3 | 100% |
| 8 | Gilbert, Joshua B (2024) Modeling item-level heterogeneous treatment effects: A tutorial with the glmer function from the lme4 package in R self | 0.894 | 7 | 4 | 71% |
| 9 | Shear, Benjamin R, Briggs, Derek C (2024) Measurement issues in causal inference | 0.874 | 5 | 2 | 100% |
| 10 | Domingue, Benjamin W, Kanopka, Klint, Trejo, Sam, Rhemtulla, Mijke,… (2022) Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outco… self | 0.843 | 3 | 3 | 100% |
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