# Interventional Direct and Indirect Effects

Multimodal health records are often compressed into low-dimensional representations and used as adjustment sets, yet variational autoencoders are trained to reconstruct data rather than to preserve what causal identification needs. In simulations, a representation that discarded a rare code affecting both treatment and mediator gave 95% intervals for the interventional indirect effect with 2% coverage at n = 3,000 under moderate confounding, while reconstruction loss did not reveal the problem; a diagnostic comparing nuisance-function predictive loss flagged it in every replicate.

Multimodal health records are often compressed into low-dimensional representations and used as adjustment sets, yet variational autoencoders are trained to reconstruct data rather than to preserve what causal identification needs. In simulations, a representation that discarded a rare code affecting both treatment and mediator gave 95% intervals for the interventional indirect effect with 2% coverage at n = 3,000 under moderate confounding, while reconstruction loss did not reveal the problem; a diagnostic comparing nuisance-function predictive loss flagged it in every replicate.