Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
Duligur Ibeling, Thomas Icard
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 ameliorative 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 960785cb-8aed-4124-a6db-4c1a1e45c243Cited by top-tier papers1
Ask how each one uses itBuilds on5
- Off-Policy Evaluation in Partially Observable EnvironmentsGuy Tennenholtz, Uri Shalit, Shie MannorAAAI 2020 · 91 citations
- Causal Imitation Learning With Unobserved ConfoundersJunzhe Zhang, Daniel Kumor, Elias BareinboimNeurIPS 2020 · 86 citations
- Off-policy Policy Evaluation For Sequential Decisions Under Unobserved ConfoundingHongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma BrunskillNeurIPS 2020 · 81 citations
- Probabilistic Reasoning Across the Causal HierarchyDuligur Ibeling, Thomas IcardAAAI 2020 · 35 citations
- A Topological Perspective on Causal InferenceDuligur Ibeling, Thomas IcardNeurIPS 2021 · 12 citations
Related papers
- Graph Contrastive Invariant Learning from the Causal PerspectiveYanhu Mo, Xiao Wang, Shaohua Fan, Chuan ShiAAAI 2024 · 32 citations
- Identifiability of Direct Effects from Summary Causal GraphsSimon Ferreira, Charles K. AssaadAAAI 2024 · 13 citations
- When is Transfer Learning Possible?My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri et al.ICML 2024
- Universal Causal Inference in a ToposSridhar MahadevanNeurIPS 2025 · 4 citations
- Formalizing and Falsifying Causal Pathways of Rare EventsAnahita Haghighat, Dominik JanzingICML 2026
