A clarification on the links between potential outcomes and do-interventions

Abstract Most of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent and therefore interchangeably uses do-interventions and the potential-outcome framework to define counterfactual outcomes. In this article, we agnostically superimpose a structural causal model and a Rubin causal model compatible with the same observations to specify the mathematical conditions under which counterfactual outcomes obtained via do-interventions and potential outcomes need to, do not need to, can, or cannot be equal (almost surely or in law). Our comparison builds upon the fact that such causal models do not have to produce the same counterfactual outcomes and highlights real-world problems where they generally cannot correspond under classical causal-inference assumptions. Then, we examine common claims and practices from the causality literature in the light of this comparison. In doing so, we aim at clarifying the links between the two causal frameworks and the interpretation of their respective counterfactuals.

Authors

Institutions

Publication Details

Journal
Journal of Causal Inference
Published
2025-01-01
DOI
https://doi.org/10.1515/jci-2024-0033
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A clarification on the links between potential outcomes and do-interventions

Lucas de Lara
Journal of Causal Inference
Advanced Causal Inference Techniques
article

A clarification on the links between potential outcomes and do-interventions

Lucas de Lara
article en

Abstract

Abstract Most of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent and therefore interchangeably uses do-interventions and the potential-outcome framework to define counterfactual outcomes. In this article, we agnostically superimpose a structural causal model and a Rubin causal model compatible with the same observations to specify the mathematical conditions under which counterfactual outcomes obtained via do-interventions and potential outcomes need to, do not need to, can, or cannot be equal (almost surely or in law). Our comparison builds upon the fact that such causal models do not have to produce the same counterfactual outcomes and highlights real-world problems where they generally cannot correspond under classical causal-inference assumptions. Then, we examine common claims and practices from the causality literature in the light of this comparison. In doing so, we aim at clarifying the links between the two causal frameworks and the interpretation of their respective counterfactuals.

Journal of Causal InferenceVol. 13(1)
Institut Élie Cartan de Lorraine (FR)
Openalex Percentile: Top 78%
Advanced Causal Inference Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.