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

Duligur Ibeling, Thomas Icard
6/25/2023
stat.MEcs.AIcs.LG

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Abstract

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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@article{ibeling2023comparing,
  title  = {Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions},
  author = {Duligur Ibeling and Thomas Icard},
  year   = {2023},
  eprint = {2306.14351},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14351v2},
  journal = {NEURIPS 2023 2023}
}

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