arXiv 28 May 2025 · Econometrics
arXiv:2505.21909 · PDF · DOI · OpenAlex · Extracted main text
How should researchers analyze randomized experiments in which the main outcome is measured in multiple ways but each measure contains some degree of error? We describe modeling approaches that enable researchers to identify causal parameters of interest, suggest ways that experimental designs can be augmented so as to make linear latent variable models more credible, and discuss empirical tests of key modeling assumptions. We show that when experimental researchers invest appropriately in multiple outcome measures, an optimally weighted index of the outcome measures enables researchers to obtain efficient and interpretable estimates of causal parameters by applying standard regression methods, and that weights may be obtained using instrumental variables regression. Maximum likelihood and generalized method of moments estimators can be used to obtain estimates and standard errors in a single step. An empirical application illustrates the gains in precision and robustness that multiple outcome measures can provide.
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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 | Kalla, J. L. & Broockman, D. E (2020) Reducing exclusionary attitudes through interpersonal conversation: Evidence from three field experiments | 0.928 | 4 | 3 | 100% |
| 2 | Stoetzer, L. F., Zhou, X., & Steenbergen, M (2025) Causal inference with latent outcomes | 0.916 | 13 | 7 | 77% |
| 3 | Anderson, M. L (2008) Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry prescho… | 0.737 | 3 | 3 | 67% |
| 4 | Imbens, G. W. & Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences | 0.644 | 2 | 2 | 100% |
| 5 | Yuan, K.-H. & Chan, W (2005) On nonequivalence of several procedures of structural equation modeling | 0.644 | 2 | 2 | 100% |
| 6 | Ansolabehere, S., Rodden, J., & Snyder, J. M (2008) The strength of issues: Using multiple measures to gauge preference stability, ideological constraint, and issue voting | 0.511 | 2 | 2 | 50% |
| 7 | Blair, G., Coppock, A., & Humphreys, M (2023) Research design in the social sciences: declaration, diagnosis, and redesign | 0.511 | 2 | 2 | 50% |
| 8 | Bollen, K. A (1989) Structural equations with latent variables, volume 210 | 0.511 | 2 | 2 | 50% |
| 9 | Loehlin, J. C (1998) Latent variable models: An introduction to factor, path, and structural equation analysis | 0.511 | 2 | 2 | 50% |
| 10 | Miao, W., Shi, X., Li, Y., & Tchetgen, E. T (2018) A confounding bridge approach for double negative control inference on causal effects | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonparametric Identification and Estimation of Causal Effects on Latent Outcomes | 1.000 | 8 | 5 |