2. 内容介绍

3. 总结

### 相关内容

【导读】哈佛大学公共卫生学院（HSPH）Miguel Hernan与Jamie Robins 教授共同编著了关于因果逻辑推断方面的书作《Causal Inference: What If》，总共分3个部分，22章，311多页，对因果推理的概念和方法做了系统性阐述，是各个领域包括经济学、健康医疗、心理学、计算机等从业人士的重要参鉴材料。

11 Why model? 139 12 IP weighting and marginal structural models 149 13 Standardization and the parametric g-formula 161 14 G-estimation of structural nested models 171 15 Outcome regression and propensity scores 183 16 Instrumental variable estimation 193 17 Causal survival analysis 209 18 Variable selection for causal inference 223

20 Treatment-confounder feedback 247 21 G-methods for time-varying treatments 257 22 Target trial emulation 277

The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.

• 附加的R代码示例和解释
• 仿真研究
• 数学的严密性，符合读者的背景
• 与课程整体结构相匹配的书本结构

1. 预先条件
1. (监督学习)回归
1. 监督学习分类
1. 无监督学习
1. (统计学习)实践
1. (统计学习)现代
1. 附录

http://kdd2020tutorial.thumedialab.com/

https://www.zhuanzhi.ai/paper/a37f27ed97e5318b30be2999e9a768c3

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