Causal Machine Learning for Healthcare and Precision Medicine
- URL: http://arxiv.org/abs/2205.11402v1
- Date: Mon, 23 May 2022 15:45:21 GMT
- Title: Causal Machine Learning for Healthcare and Precision Medicine
- Authors: Pedro Sanchez and Jeremy P. Voisey and Tian Xia and Hannah I. Watson
and Alison Q. ONeil and Sotirios A. Tsaftaris
- Abstract summary: Causal machine learning (CML) has experienced increasing popularity in healthcare.
We explore how causal inference can be incorporated into different aspects of clinical decision support systems.
- Score: 16.846051073534966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causal machine learning (CML) has experienced increasing popularity in
healthcare. Beyond the inherent capabilities of adding domain knowledge into
learning systems, CML provides a complete toolset for investigating how a
system would react to an intervention (e.g.\ outcome given a treatment).
Quantifying effects of interventions allows actionable decisions to be made
whilst maintaining robustness in the presence of confounders. Here, we explore
how causal inference can be incorporated into different aspects of clinical
decision support (CDS) systems by using recent advances in machine learning.
Throughout this paper, we use Alzheimer's disease (AD) to create examples for
illustrating how CML can be advantageous in clinical scenarios. Furthermore, we
discuss important challenges present in healthcare applications such as
processing high-dimensional and unstructured data, generalisation to
out-of-distribution samples, and temporal relationships, that despite the great
effort from the research community remain to be solved. Finally, we review
lines of research within causal representation learning, causal discovery and
causal reasoning which offer the potential towards addressing the
aforementioned challenges.
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