mlsanityData to 14 Oct 2026

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

arXiv 2306.14351 ↗First posted 25 Jun 2023stat.ME cs.AI cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 47 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

15 of 47 references.

References · 15

  1. 2017Causal Consistency of Structural Equation Modelscited by 118
  2. 2018Confounding-Robust Policy Improvementcited by 76
  3. 2013Causal Networks: Semantics and Expressivenesscited by 67
  4. 2019Off-Policy Evaluation in Partially Observable Environmentscited by 66
  5. 2019Approximate Causal Abstractioncited by 63
  6. 2020Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confoundingcited by 61
  7. 2022Causal Imitation Learning with Unobserved Confounderscited by 41
  8. 2017Nested Markov Properties for Acyclic Directed Mixed Graphscited by 39
  9. 2015Margins of discrete Bayesian networkscited by 26
  10. 2019A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effectscited by 19
  11. 2020Probabilistic Reasoning across the Causal Hierarchycited by 19
  12. 2014Causal Inference with a Graphical Hierarchy of Interventionscited by 11
3 more
  1. 2021A Topological Perspective on Causal Inferencecited by 10
  2. 2021Is Causal Reasoning Harder than Probabilistic Reasoning?cited by 10
  3. 2021Causal Modeling With Infinitely Many Variablescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 3.

11 papers.

  1. 2024Causal Estimation of Memorisation Profilescited by 20
  2. 2023Efficient Computation of Counterfactual Boundscited by 8
  3. 2025What is causal about causal models and representations?cited by 6
  4. 2024On Probabilistic and Causal Reasoning with Summation Operatorscited by 2
  5. 2025Modeling Discrimination with Causal Abstractioncited by 2
  6. 2025Probabilistic Modelling is Sufficient for Causal Inferencecited by 2
  7. 2025Causal Feature Learning in the Social Sciencescited by 0
  8. 2025What is a good matching of probability measures? A counterfactual lens on transport mapscited by 0
  9. 2026Causal Transfer in Medical Image Analysiscited by 0
  10. 2026The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchycited by 0
  11. 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0