Correct deconfounding can support causal brain-behavioural predictive modeling

Brisbane Convention & Exhibition Centre 

Machine Learning (ML) in neuroscientific research offers opportunities to understand neuronal underpinnings of behaviour in health and disease. While ML applications often aim to advance neuroscientific understanding, they are frequently judged solely based on accuracy, fueling a "performance race" in model development. Problematically, such high accuracies, especially in neuroscience, are often achieved by relying on confounder information. This reliance can exacerbate challenges, including unreliable predictions, non-reproducibility, limited generalizability, and non-interpretability of ML results.
In clinical settings, randomized control trials (RCT) are a well-established tool to mitigate confounding influences to obtain cause-effect insights. In contrast, ML solutions are typically applied to observational data, which require post-hoc statistical confounder control, treating confounding as a purely associative phenomenon. However, distinguishing confounding effects from mediators, colliders or proxies, requires understanding of the directionality of effects, i.e. causal reasoning, to prevent faulty adjustments that may introduce spurious correlations (Hamdan, 2023). Additionally, integrating causal reasoning into neuroscientific ML workflows can facilitate investigation of brain-behavioural cause-effect relationships, akin to RCTs in clinical studies.
Here, using a brain-behavioural predictive example, we illustrate how to leverage domain knowledge to build a Directed Acyclic Graph (DAG) of causal relations and how this DAG can serve as a basis for confounder adjustment in ML analysis, enabling provisional causal insights (Pearl & Mackenzie, 2018).