# On the Causal Interpretation of Randomized Interventional Indirect Effects

arXiv: https://arxiv.org/abs/2203.00245 · HTML: https://mlsanity.com/paper/2203.00245/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [General targeted machine learning for modern causal mediation analysis](https://mlsanity.com/paper/2408.14620/) (2024)
- [Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding](https://mlsanity.com/paper/2604.01501/) (2026)

## Abstract (arXiv)

Identification of standard mediated effects such as the natural indirect effect relies on heavy causal assumptions. By circumventing such assumptions, so-called randomized 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, randomized interventional indirect effects do not satisfy such criteria. I additionally discuss alternative causal interpretations of such effects.