Informative inference on the average treatment effect with instrumental variables
Abstract
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.
Related
- Estimating Average Treatment Effects with Projected Instrumental Variables
- Distribution-free predictive inference with instrumental variables
- Semiparametric Efficient Inference under Non-Informative Complex Survey Designs
- Coherent and robust Bayesian inference under informative sampling via sample-level weight models
- Longitudinal causal inference under informative missingness using the self-censoring model
BibTeX
@misc{locher2026informative,
title = {{Informative inference on the average treatment effect with instrumental variables}},
author = {Luca Locher and Thomas Richardson and Mats Stensrud},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33254},
eprint = {2610.33254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33254},
}
RIS
TY - UNPB
TI - Informative inference on the average treatment effect with instrumental variables
AU - Luca Locher
AU - Thomas Richardson
AU - Mats Stensrud
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.33254
UR - https://arxiv.org/abs/2610.33254
AB - 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.
ER -