Application of propensity score and random forest in the causal inference: the causality of stroke

Yanke Mao; Tianyi Zhang; Anyang Wu; Jayden Szeto; Huange Cui

doi:10.1117/12.3009382

16 October 2023 Application of propensity score and random forest in the causal inference: the causality of stroke

Yanke Mao, Tianyi Zhang, Anyang Wu, Jayden Szeto, Huange Cui

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280338 (2023) https://doi.org/10.1117/12.3009382
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Stroke may be associated with many factors of our health. In this paper, we use propensity score matching, inverse probability weighting, outcome modeling and doubly robust to estimate the average treatment effect for evaluating the causality. Our estimates are based on the data about 5110 persons with 12 features. For these persons, we focus on five features that are BMI, average glucose level, hypertension, heart disease and age. The results show that age, heart disease, and hypertension have relatively significant causality with stroke.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanke Mao, Tianyi Zhang, Anyang Wu, Jayden Szeto, and Huange Cui "Application of propensity score and random forest in the causal inference: the causality of stroke", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280338 (16 October 2023); https://doi.org/10.1117/12.3009382

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