[2603.01119] Robust Weighted Triangulation of Causal Effects Under Model Uncertainty

Statistics > Methodology arXiv:2603.01119 (stat) [Submitted on 1 Mar 2026] Title:Robust Weighted Triangulation of Causal Effects Under Model Uncertainty Authors:Rohit Bhattacharya, Ina Ocelli, Ted Westling View a PDF of the paper titled Robust Weighted Triangulation of Causal Effects Under Model Uncertainty, by Rohit Bhattacharya and 2 other authors View PDF HTML (experimental) Abstract:A fundamental challenge in causal inference with observational data is correct specification of a causal model. When there is model uncertainty, analysts may seek to use estimates from multiple candidate models that rely on distinct, and possibly partially overlapping, sets of identifying assumptions to infer the causal effect, a process known as triangulation. Principled methods for triangulation, however, remain underdeveloped. Here, we develop a framework for causal effect triangulation that combines model testability methods from causal discovery with statistical inference methods from semiparametric theory, while avoiding explicit model selection and post-selection inference problems. We propose a triangulation functional that combines identified functionals from each model with data-driven measures of model validity. We provide a bound on the distance of the functional from the true causal effect along with conditions under which this distance can be taken to zero. Finally, we derive valid statistical inference for this functional. Our framework formalizes robustness under causal plur...