Methods of causal effect estimation for high‐dimensional treatments: A radiotherapy simulation study
This work shows that leveraging sparse causal inference methods can benefit both the identification of regions of given dose-response and the estimation of treatment effects. Causal inference methodologies provide a powerful approach to account for limitations in voxel-based analysis. Adapting causal inference methodologies to the analysis of clinical radiotherapy treatment-outcome data could lead to new and impactful insights on the causes of treatment complications.
Authors
- Alexander Jenkins (ORCID: https://orcid.org/0000-0002-8517-5154)
- Eliana Vásquez Osorio (ORCID: https://orcid.org/0000-0003-0741-994X)
- Matthew Sperrin (ORCID: https://orcid.org/0000-0002-5351-9960)
- Alan McWilliam (ORCID: https://orcid.org/0000-0002-7344-2868)
- Marcel van Herk
- Andrew Green (ORCID: https://orcid.org/0000-0002-8297-0953)
Institutions
- European Bioinformatics Institute (GB)
- University of Manchester (GB)
- Cancer Research UK Manchester Institute (GB)
- The Christie NHS Foundation Trust (GB)
- Imperial College London (GB)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1002/mp.17919
- Citations
- 3
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 7.89
Funders
- UK Research and Innovation
- Cancer Research UK
- National Institute for Health and Care Research
- Manchester Biomedical Research Centre