Robust Weighted Triangulation of Causal Effects Under Model Uncertainty

2026-03-01

AI Summary

This study addresses the inferential bias arising from misspecification of a single causal model under model uncertainty. The authors propose a weighted triangulation framework that integrates identification functionals from multiple candidate causal models through a data-driven measure of model validity, enabling robust causal effect estimation without explicit model selection. This approach uniquely bridges testability in causal discovery with semiparametric inference, formalizing robustness under causal pluralism without requiring consensus among models or reliance on any single specification. Theoretically, the proposed functional is shown to converge to the true causal effect with high probability. Both simulation studies and empirical analyses demonstrate the method’s robustness and effectiveness.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 pluralism without requiring agreement across models or commitment to a single specification. We demonstrate its performance through simulations and an empirical application.

Problem

• causal inference
• model uncertainty
• triangulation
• observational data
• causal effect

Innovation

• causal triangulation
• model uncertainty
• semiparametric inference
• causal discovery
• robustness