# An Interventionist Approach to Mediation Analysis

@article{Robins2020AnIA, title={An Interventionist Approach to Mediation Analysis}, author={James M. Robins and Thomas S. Richardson and Ilya Shpitser}, journal={Probabilistic and Causal Inference}, year={2020} }

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…

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## References

SHOWING 1-10 OF 38 REFERENCES

### CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS.

- MathematicsAnnals of statistics
- 2016

A unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions is given, and it is shown that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy.

### Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding

- PsychologyCogn. Sci.
- 2013

This article shows the generality of a general counterfactual framework for reasoning about causality first described by Neyman and Rubin and linked to causal graphical models by Robins (1986) and Pearl (2006) by proving a novel result which allows mediation analysis to be applied to longitudinal settings with unobserved confounders.

### Alternative Graphical Causal Models and the Identification of Direct E!ects

- Computer Science
- 2010

This paper analyzes various measures of the ‘direct’ causal effect, focussing on the pure direct effect (PDE), and introduces the Minimal Counterfactual Model (MCM) which is referred to as ‘minimal’ because it imposes the minimal counterfactual independence assumptions.

### A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects

- MathematicsAISTATS
- 2019

This paper uses po-calculus to give a complete identification algorithm for conditional path-specific effects with applications to problems in mediation analysis and algorithmic fairness.

### Generalizing causal inferences from randomized trials: counterfactual and graphical identification

- Psychology
- 2019

When engagement with a randomized trial is driven by factors that affect the outcome or when trial engagement directly affects the outcome independent of treatment, the average treatment effect among…

### Multivariate Counterfactual Systems and Causal Graphical Models

- Philosophy
- 2020

This chapter shows that d-separation} provides direct insight into an earlier causal model originally described in terms of potential outcomes and event trees, which leads to a simplification of the do-calculus that clarifies and separates the underlying concepts.

### Modeling Interference Via Symmetric Treatment Decomposition

- Computer Science
- 2017

A new approach to decomposing the spillover effect into direct and indirect components that extends the DAG based treatment decomposition approach to mediation to causal chain graph models and has an identifying functional, which is called the symmetric mediation formula that generalizes the mediation formula in DAGs.

### Separable Effects for Causal Inference in the Presence of Competing Events

- MathematicsJournal of the American Statistical Association
- 2020

The new separable effects to study the causal effect of a treatment on an event of interest are proposed and do not require cross-world contrasts or hypothetical interventions to prevent death.

### Time‐dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model

- MathematicsBiometrical journal. Biometrische Zeitschrift
- 2019

This paper demonstrates that combining the g-formula with the additive hazards model and a sequential linear model for the mediator process results in simple and interpretable expressions for direct and indirect effects in terms of relative survival as well as cumulative hazards.

### Conditional separable effects

- Economics
- 2020

Alternative estimands are formulated, the conditional separable effects, that have a natural causal interpretation under assumptions that can be falsified in a randomized experiment and can be identified without relying on unfalsifiable assumptions.