# Disparate Effect Of Missing Mediators On Transportability of Causal Effects

arXiv: https://arxiv.org/abs/2403.08638 · HTML: https://mlsanity.com/paper/2403.08638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Covariate Selection for Generalizing Experimental Results: Application to Large-Scale Development Program in Uganda](https://mlsanity.com/paper/1909.02669/) (2019)
- [Variance-based sensitivity analysis for weighting estimators result in more informative bounds](https://mlsanity.com/paper/2208.01691/) (2022)
- [Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/2110.00107/) (2021)
- [Transporting stochastic direct and indirect effects to new populations](https://mlsanity.com/paper/1903.03690/) (2019)
- [Generalizing treatment effects with incomplete covariates: identifying assumptions and multiple imputation algorithms](https://mlsanity.com/paper/2104.12639/) (2021)
- [Estimating subgroup effects in generalizability and transportability analyses](https://mlsanity.com/paper/2109.14075/) (2021)

## Abstract (arXiv)

Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when applied to different populations as a result of factors that mediate the effects. However, when mediators are missing in the population where the effect is to be transported, these estimates could be biased. We study this issue of missing mediators, motivated by challenges in public health, wherein mediators can be missing, not at random. We propose a sensitivity analysis framework that quantifies the impact of missing mediator data on transported mediation effects. This framework enables us to identify the settings under which the conditional transported mediation effect is rendered insignificant for the subgroup with missing mediator data. Specifically, we provide the bounds on the transported mediation effect as a function of missingness. We then apply the framework to longitudinal data from the Moving to Opportunity Study, a large-scale housing voucher experiment, to quantify the effect of missing mediators on transport effect estimates of voucher receipt, an upstream intervention on living location, in childhood on subsequent risk of mental health or substance use disorder mediated through parental health across sites. Our findings provide a tangible understanding of how much missing data can be withstood for unbiased effect estimates.