Tymon Słoczyński, Liyang Sun, S. Derya Uysal
arXiv 14 May 2026 · Econometrics
arXiv:2605.15115 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variables (IV) methods are central to applied microeconomics. While classical approaches assume linear models with constant effects, recent literature has shifted toward the local average treatment effect (LATE) framework to accommodate heterogeneous treatment effects. This paper provides a practical guide to aligning empirical practice with recent theory. We first examine how different specifications with covariates lead to distinct weighted averages of covariate-specific LATEs. We then discuss how parametric misspecification can undermine the causal interpretation of these estimands and suggest flexible specifications as essential robustness checks. Finally, we review formal tests for LATE assumptions and methods robust to monotonicity violations. We provide a guide to software implementations to help researchers apply the methods in practice.
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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 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 1.000 | 20 | 3 | 100% |
| 2 | Angrist, J. D. and Imbens, G. W (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity | 1.000 | 5 | 3 | 100% |
| 3 | Soczyński, T (2026) When should we (not) interpret linear IV estimands as LATE? | 0.874 | 11 | 2 | 100% |
| 4 | Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J.,… (2012) The Oregon Health Insurance Experiment: Evidence from the first year | 0.874 | 9 | 2 | 100% |
| 5 | Soczyński, T., Uysal, S. D., and Wooldridge, J. M (2025) Abadie's kappa and weighting estimators of the local average treatment effect self | 0.874 | 5 | 2 | 100% |
| 6 | Blandhol, C., Bonney, J., Mogstad, M., and Torgovitsky, A (2026) When is TSLS actually LATE? | 0.811 | 4 | 2 | 100% |
| 7 | Uysal, S. D (2011) Doubly robust IV estimation of the local average treatment effect self | 0.811 | 4 | 2 | 100% |
| 8 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 9 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 10 | Soczyński, T., Uysal, S. D., and Wooldridge, J. M (2022) Doubly robust estimation of local average treatment effects using inverse probability weighted regression adjustment self | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.