SearcharxivSearch

arXiv · 2509.02045

Interpretational errors with instrumental variables

Abstract

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Luca Locher, Mats J. Stensrud, Aaron L. Sarvet. 2025-09-02. Interpretational errors with instrumental variables. https://arxiv.org/abs/2509.02045

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM