An Interventionist Approach to Mediation Analysis
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Abstract (arXiv)
Judea Pearl's insight that, when errors are assumed independent, the Pure (aka Natural) Direct Effect (PDE) is non-parametrically identified via the Mediation Formula was `path-breaking' in more than one sense! In the same paper Pearl described a thought-experiment as a way to motivate the PDE. Analysis of this experiment led Robins & Richardson to a novel way of conceptualizing direct effects in terms of interventions on an expanded graph in which treatment is decomposed into multiple separable components. We further develop this novel theory here, showing that it provides a self-contained framework for discussing mediation without reference to cross-world (nested) counterfactuals or interventions on the mediator. The theory preserves the dictum `no causation without manipulation' and makes questions of mediation empirically testable in future Randomized Controlled Trials. Even so, we prove the interventionist and nested counterfactual approaches remain tightly coupled under a Non-Parametric Structural Equation Model except in the presence of a `recanting witness.' In fact, our analysis also leads to a simple sound and complete algorithm for determining identification in the (non-interventionist) theory of path-specific counterfactuals.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 3; 2025: 0; 2026: 2.
10 papers.
- 2022DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learningcited by 10
- 2021Causal Effect Identification with Context-specific Independence Relations of Control Variablescited by 8
- 2021Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curvescited by 5
- 2024A hierarchical decomposition for explaining ML performance discrepanciescited by 5
- 2024On Discovery of Local Independence over Continuous Variables via Neural Contextual Decompositioncited by 4
- 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventionscited by 1
- 2024General targeted machine learning for modern causal mediation analysiscited by 1
- 2021Path-specific Effects Based on Information Accounts of Causalitycited by 0
- 2026Formalizing and falsifying causal pathways of rare eventscited by 0
- 2026Bayesian Mediation Analysis for Individualized Treatment Rulescited by 0
- 2026Bayesian Mediation Analysis for Individualized Treatment Rules30 Jul 2026
- 2026Formalizing and falsifying causal pathways of rare events29 May 2026
- 2024General targeted machine learning for modern causal mediation analysis26 Aug 2024
- 2024On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition12 May 2024
- 2024A hierarchical decomposition for explaining ML performance discrepancies22 Feb 2024
- 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions16 Jun 2023
- 2022DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning10 Oct 2022
- 2021Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves6 Nov 2021
- 2021Causal Effect Identification with Context-specific Independence Relations of Control Variables22 Oct 2021
- 2021Path-specific Effects Based on Information Accounts of Causality6 Jun 2021