Causal inference and counterfactual prediction in machine learning for actionable healthcare
By Mattia C. F. Prosperi, Yi Guo, M. Sperrin, James S. Koopman, Jae Min, Xing He, Shannan N. Rich, Mo Wang, Iain E. Buchan, Jiang Bian. Nature Machine Intelligence, 2020
432 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e9540a9611fd85d556308deec5e3c3def017667e
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In Artificial Intelligence
- Bernd Malle
- Anna Saranti
- Andreas Holzinger
- Shakir Mohamed
- Bastian Pfeifer
- Nenad Tomašev
- Stefano Ermon
- Sergey Levine
In Economics and Econometrics
In Statistics and Probability
Citations in the corpus
- Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI (2021, background): “Transportable models can be integrated into clinical guidelines to augment domain experts with ‘‘action-savvy’’ predictions, in pursuit of better precision medicine [21].”
- Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (2021, background): “Compounding these issues, queer people often find it more difficult to ask for help and articulate their distress (McDermott 2015) and face systemic barriers to treatment (Romanelli and Hudson 2017).”