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.

翻译：观测数据因果推断中的一个基本挑战在于因果模型的正确设定。当存在模型不确定性时，分析者可能希望利用多个候选模型的估计结果来推断因果效应，这些模型依赖于不同且可能部分重叠的识别假设集，这一过程称为三角化。然而，三角化的原则性方法仍然发展不足。本文提出了一种因果效应三角化框架，该框架将因果发现中的模型可检验性方法与半参数理论中的统计推断方法相结合，同时避免了显式的模型选择及选择后推断问题。我们提出了一个三角化泛函，该泛函将每个模型的识别泛函与数据驱动的模型有效性度量相结合。我们给出了该泛函与真实因果效应之间距离的上界，以及该距离可趋于零的条件。最后，我们为该泛函推导了有效的统计推断方法。我们的框架在不要求模型间达成一致或承诺单一设定的前提下，形式化了因果多元主义下的稳健性。我们通过模拟和实证应用展示了其性能。