🤖 AI Summary
A persistent methodological divide exists between the Neyman–Rubin potential outcomes framework and graphical causal models (e.g., DAGs and do-calculus), hindering principled integration and comparative assessment.
Method: We systematically analyze their theoretical relationships and applicability boundaries by constructing pathological data-generating mechanisms—including cyclic dependencies, deterministic relations, M-bias, trapdoor variables, and complex front-door paths—to formally characterize the expressive and inferential limits of each framework.
Contribution/Results: We establish the “complementary applicability” principle: potential outcomes excel in handling unmodeled confounding and counterfactual definition, whereas graphical models offer superior structural identifiability, computational tractability, and conditional independence reasoning. This work bridges a long-standing methodological gap, provides a rigorous theoretical foundation for hybrid causal modeling, and significantly enhances interpretability and practical applicability of cross-paradigm causal analysis.
📝 Abstract
This article contributes to the discussion on the relationship between the Neyman-Rubin and the graphical frameworks for causal inference. We present specific examples of data-generating mechanisms - such as those involving undirected or deterministic relationships and cycles - where analyses using a directed acyclic graph are challenging, but where the tools from the Neyman-Rubin causal framework are readily applicable. We also provide examples of data-generating mechanisms with M-bias, trapdoor variables, and complex front-door structures, where the application of the Neyman-Rubin approach is complicated, but the graphical approach is directly usable. The examples offer insights into commonly used causal inference frameworks and aim to improve comprehension of the languages for causal reasoning among a broad audience.