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Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions

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arxiv 2306.14351 v2 pith:JENGH4JC submitted 2023-06-25 stat.ME cs.AIcs.LGcs.LOstat.ML

classification stat.MEcs.AIcs.LGcs.LOstat.ML
keywords causalframeworksalgebraicimpliedmodelperspectiveprinciplesrepresentable
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The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM. A key result then shows that every RCM -- including those that violate algebraic principles implied by the SCM framework -- emerges as an abstraction of some representable RCM. Finally, we illustrate the power of this conciliatory perspective by pinpointing an important role for SCM principles in classic applications of RCMs; conversely, we offer a characterization of the algebraic constraints implied by a graph, helping to substantiate further comparisons between the two frameworks.

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