Causal machine learning offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles.
Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Continuous-time modeling of counterfactual outcomes using neural controlled differential equations. InHess, K., Melnychuk, V., Frauen, D. & Feuerriegel, S. Bayesian neural controlled differential equations for treatment effect estimation. InHatt, T., Berrevoets, J., Curth, A., Feuerriegel, S. & van der Schaar, M. Combining observational and randomized data for estimating heterogeneous treatment effects.
Banerji, C. R., Chakraborti, T., Harbron, C. & MacArthur, B. D. Clinical AI tools must convey predictive uncertainty for each individual patient. 3425–3433 . Keogh, R. H. & van Geloven, N. Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data. Preprint at
Jesson, A., Mindermann, S., Shalit, U. & Gal, Y. Identifying causal-effect inference failure with uncertainty-aware models. In 11637–11649 .
Source: Education Headlines (educationheadlines.net)
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