Interpretable Machine Learning Techniques for Causal Inference Using Balancing Scores as Meta-features

Yasunobu Nohara, Koji Iihara, Naoki Nakashima

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

2 被引用数 (Scopus)

抄録

Estimating individual causal effect is important for decision making in many fields especially for medical interventions. We propose an interpretable and accurate algorithm for estimating causal effects from observational data. The proposed scheme is combining multiple predictors' outputs by an interpretable predictor such as linear predictor and if then rules. We secure interpretability using the interpretable predictor and balancing scores in causal inference studies as meta-features. For securing accuracy, we adapt machine learning algorithms for calculating balancing scores. We analyze the effect of t-PA therapy for stroke patients using real-world data, which has 64,609 records with 362 variables and interpret results. The results show that cross validation AUC of the proposed scheme is little less than original machine learning scheme; however, the proposed scheme provides interpretability that t-PA therapy is effective for severe patients.

本文言語英語
ホスト出版物のタイトル40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ4042-4045
ページ数4
ISBN(電子版)9781538636466
DOI
出版ステータス出版済み - 10月 26 2018
イベント40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, 米国
継続期間: 7月 18 20187月 21 2018

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2018-July
ISSN(印刷版)1557-170X

その他

その他40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
国/地域米国
CityHonolulu
Period7/18/187/21/18

!!!All Science Journal Classification (ASJC) codes

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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