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On the causal interpretation of randomised interventional indirect effects

Caleb H. Miles

2023Journal of the Royal Statistical Society Series B (Statistical Methodology)26 citationsDOI

Abstract

Abstract Identification of standard mediated effects such as the natural indirect effect relies on heavy causal assumptions. By circumventing such assumptions, so-called randomised interventional indirect effects have gained popularity in the mediation literature. Here, I introduce properties one might demand of an indirect effect measure in order for it to have a true mediational interpretation. For instance, the sharp null criterion requires an indirect effect measure to be null whenever no individual-level indirect effect exists. I show that without stronger assumptions, randomised interventional indirect effects do not satisfy such criteria. I additionally discuss alternative causal interpretations of such effects.

Topics & Concepts

Indirect effectInterpretation (philosophy)Null hypothesisMediationNull (SQL)EconometricsIdentification (biology)PsychologyMathematicsComputer scienceData miningLawBiologyBotanyPolitical scienceProgramming languageAdvanced Causal Inference TechniquesStatistical Methods in Clinical TrialsHealth Systems, Economic Evaluations, Quality of Life