Robust Weighted Triangulation of Causal Effects Under Model Uncertainty

TL;DR

This paper introduces a new framework for causal effect triangulation that combines multiple models and data-driven validity measures, providing robust inference under model uncertainty without needing model agreement.

Contribution

It develops a formalized, model-agnostic triangulation method integrating causal discovery and semiparametric inference, avoiding explicit model selection issues.

Findings

Framework achieves robustness without model agreement.

Provides bounds and conditions for accurate causal effect estimation.

Demonstrates effectiveness through simulations and empirical data.

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.