Luca Locher, Mats J. Stensrud, Aaron L. Sarvet
arXiv 2 Sep 2025 · Econometrics
arXiv:2509.02045 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variables (IV) are often used to identify causal effects in observational settings and experiments subject to non-compliance. Under canonical assumptions, IVs allow us to identify a so-called local average treatment effect (LATE). The use of IVs is often accompanied by a pragmatic decision to abandon the identification of the causal parameter that corresponds to the original research question and target the LATE instead. This pragmatic decision presents a potential source of error: an investigator mistakenly interprets findings as if they had made inference on their original causal parameter of interest. We conducted a systematic review and meta-analysis of patterns of pragmatism and interpretational errors in the applied IV literature published in leading journals of economics, political science, epidemiology, and clinical medicine (n = 309 unique studies). We found that a large fraction of studies targeted the LATE, although specific interest in this parameter was rare. Of these studies, 61% contained claims that mistakenly suggested that another parameter was targeted -- one whose value likely differs, and could even have the opposite sign, from the parameter actually estimated. Our findings suggest that the validity of conclusions drawn from IV applications is often compromised by interpretational errors.
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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 | Sonja A Swanson and Miguel A Hernán (2018) The challenging interpretation of instrumental variable estimates under monotonicity | 0.928 | 4 | 3 | 100% |
| 2 | Joshua D. Angrist, Guido W. Imbens, and Donald B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.811 | 4 | 2 | 100% |
| 3 | James J. Heckman and Sergio Urzúa (2010) Comparing IV with structural models: What simple IV can and cannot identify | 0.644 | 2 | 2 | 100% |
| 4 | Guido W. Imbens (2022) Causality in Econometrics: Choice vs Chance | 0.644 | 2 | 2 | 100% |
| 5 | Sandra Sequeira, Nathan Nunn, and Nancy Qian (2020) Immigrants and the Making of America | 0.644 | 2 | 2 | 100% |
| 6 | Sonja A. Swanson, Miguel A. Hernán, Matthew Miller, James M. Robins,… (2018) Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, T… | 0.644 | 2 | 2 | 100% |
| 7 | Amy Finkelstein, Sarah Taubman, Bill Wright, Mira Bernstein, Jonatha… (2012) The Oregon Health Insurance Experiment: Evidence from the First Year* | 0.511 | 2 | 1 | 100% |
| 8 | Judea Pearl (2009) Causality | 0.511 | 2 | 1 | 100% |
| 9 | Jason Abaluck, Mauricio Caceres Bravo, Peter Hull, and Amanda Starc Mortality Effects and Choice Across Private Health Insurance Plans* | 0.405 | 1 | 1 | 100% |
| 10 | Daron Acemoglu, Giuseppe De Feo, and Giacomo Davide De Luca (2020) Weak States: Causes and Consequences of the Sicilian Mafia | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Evaluating Counterfactual Policies Using Instruments | 0.405 | 1 | 1 |