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Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
勇敢和真实的因果推理。轻松而严谨的方法来学习影响评估和敏感性分析。
Causal Inference for the Brave and True 这本书由巴西Nubank的Staff Data Scientist Matheus Facure 所著。该书用平实的语言和严谨的数学,以及实用的Python代码,结合经济学与社会学的策略评估和敏感性分析应用,对因果推断最新的概念、理论及实践进行了非常全面的介绍,既适合初学者入门,同时也适合技术管理专家回顾相关领域的整体知识。该书英文原版的Jupyter Notebooks可以由该Github地址获取。
本书主要基础是计量经济学,吸收了非常多学者,包括 Joshua Angrist, Alberto Abadie,Christopher Walters,Miguel Hernan 和 Jamie Robins 等,在这方面的最新研究,主要参考了以下资料:
Cross-Section Econometrics
Mastering Mostly Harmless Econometrics
Mostly Harmless Econometrics
Mastering 'Metrics
Causal Inference Book
https://matheusfacure.github.io/python-causality-handbook/landing-page.html
Causal Inference for The Brave and True
PART I - THE YANG
17 - Predictive Models 101
18 - Heterogeneous Treatment Effects and Personalization
19 - Evaluating Causal Models
20 - Plug-and-Play Estimators
21 - Meta Learners
22 - Debiased/Orthogonal Machine Learning
23 - Challenges with Effect Heterogeneity and Nonlinearity
24 - The Difference-in-Differences Saga
APPENDIX
Debiasing with Orthogonalization
Debiasing with Propensity Score
When Prediction Fails
Why Prediction Metrics are Dangerous For Causal Models
CONTRIBUTE
中文版
https://github.com/xieliaing/CausalInferenceIntro
https://gitee.com/xieliaing/causal-inference-intro-gitee/tree/master/chapters
苏格拉底大王 (I am serious with Socrates.) 组长 楼主 2022-05-25 11:02:15
我看了感觉确实不错,对于理解原理很有帮助,是属于重新造轮子类型的书。python社区还是缺乏因果推断的包。
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