Luca Locher | Searcharxiv

arXiv2026

Informative inference on the average treatment effect with instrumental variables

Policy decisions often depend on whether the average treatment effect (ATE) exceeds a specific cutoff. In instrumental variable (IV) settings, point identification of the ATE requires strong assumptions that are often difficult to justify. Therefore, many researchers shift their attention to identification of the local (L) ATE or target bounds of the ATE. Both strategies have well-known drawbacks: the substantive relevance of the LATE is frequently questioned, and the bounds are often too wide to be informative. We propose a simple sensitivity analysis that allows researchers to maintain focus on the ATE, and then discuss its connection to related bounding procedures. Based on these results, researchers can decide, in a principled manner, whether the sign of the ATE is inferable under assumptions they deem credible. The proposed strategies are easy to apply, and we illustrate their value with two real-data examples. The R package ivate implements our procedures.

stat.ME↗

arXiv2025

Interpretational errors with instrumental variables

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.

econ.EM↗