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Science

G-methods for time-varying treatments (Causal inference: What if, Chapter 21-1)
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Treatment-Confounder feedback(Causal inference: What if, Chapter 20)
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3.3k
Time-varying treatment(Causal inference: What if, Chapter 19)
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3.4k
Variable selection for causal inference(Causal inference: What if, Chapter 18)
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4.7k
Causal Survival Analysis(Causal inference: What if, Chapter 17)
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5.4k
Instrumental variable estimation(Causal inference: What if, Chapter 16)
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5k
Outcome regression and propensity scores (Causal inference: What if, Chapter 15)
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G-estimation and Structural nested mean model(Causal inference: What if, Chapter 14)
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5.2k
Standardization and the parametric G-formula(Causal inference: What if, Chapter 13)
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5.7k
IP Weighting and Marginal Structural Models(Causal inference: What if, Chapter 12)
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5.6k
Effect modification(Causal inference: What if, Chapter 4)
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6.1k
テンソル分解を用いた教師無し学習による変数選択法を用いたCOVID-19治療薬の探索
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Linear non-Gaussian models with latent variables for causal discovery (for the NeurIPS2020 Workshop)
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560
Predicting the Wind: Data Science in Wind Resource Assessment
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86
20201121_oldpaperreading_computing_machinery_and_intelligence
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170
深層学習をつかった画像スタイル変換の話と今までの歴史
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460
対応分析
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170