Causal machine learning for extracting insights from observational radiotherapy data.
Where this comes from
- Record sourced from PubMed, PMID 42736387.
- Also identified by DOI 10.1038/s41746-026-03214-z and PMC identifier 13575218.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
While clinical trials are the gold standard for determining causative side effects from treatment, some trials are too logistically or ethically challenging to complete. Many side effects of treatments are learned from retrospective analyses; however, these can be confounded by other random variables. Causal and explainable machine learning tools are promising for teasing apart confounders from real treatment side effects in observational data.