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Title: GitHub - partrita/causal-inference-in-python-code: 책 "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024)의 내용을 정리

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X Title: GitHub - partrita/causal-inference-in-python-code: 책 "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024)의 내용을 정리

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인과추론의 데이터과학https://www.youtube.com/@causaldatascience
https://github.com/partrita/causal-inference-in-python-code#챕터별-자료
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인과추론의 데이터과학 Youtubehttps://www.youtube.com/@causaldatascience
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원서https://github.com/matheusfacure/python-causality-handbook
번역서https://github.com/CausalInferenceLab/Causal-Inference-with-Python
Causal Inference Stanford STATS 361 강의 노트https://web.stanford.edu/~swager/stats361.pdf
Causal Inference for Statistics, Social, and Biomedical Scienceshttps://www.amazon.com/Causal-Inference-Statistics-Biomedical-Sciences/dp/0521885884
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https://github.com/partrita/causal-inference-in-python-code#6장-이질적-처치효과
Nubank fklearnhttps://github.com/nubank/fklearn
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EconML 공식문서https://econml.azurewebsites.net/
CausalML 공식문서https://causalml.readthedocs.io/en/latest/about.html
Metalearners for estimating heterogeneous treatment effects using machine learninghttps://www.pnas.org/doi/full/10.1073/pnas.1804597116
Double/Debiased/Neyman Machine Learning of Treatment Effectshttps://www.aeaweb.org/articles?id=10.1257/aer.p20171038
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Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspectshttps://www.aeaweb.org/articles?id=10.1257/jel.20191450
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Synthetic Difference-in-Differenceshttps://www.aeaweb.org/articles?id=10.1257/aer.20190159
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Modelshttps://www.cambridge.org/core/journals/political-analysis/article/generalized-synthetic-control-method-causal-inference-with-interactive-fixed-effects-models/B63A8BD7C239DD4141C67DA10CD0E4F3
A Bayesian Alternative to Synthetic Control for Comparative Case Studieshttps://www.cambridge.org/core/journals/political-analysis/article/bayesian-alternative-to-synthetic-control-for-comparative-case-studies/C23BD67E4BBBB8C88ADAEAE169696A45
CausalImpact Githubhttps://github.com/google/CausalImpact?tab=readme-ov-file
https://github.com/partrita/causal-inference-in-python-code#10장-스위치백-실험
Synthetic Controls for Experimental Designhttps://economics.mit.edu/sites/default/files/2023-02/synthetic%20experiments.pdf
Designing Experiments with Synthetic Controlshttps://mackinstitute.wharton.upenn.edu/wp-content/uploads/2020/03/Wernerfelt-Nils-Doudchenko-Nick-Gilinson-David-and-Taylor-Sean_Designing-Experiments-with-Synthetic-Controls.pdf
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controlshttps://proceedings.neurips.cc/paper/2021/hash/48d23e87eb98cc2227b5a8c33fa00680-Abstract.html
Design and Analysis of Switchback Experimentshttps://arxiv.org/abs/2009.00148
https://github.com/partrita/causal-inference-in-python-code#11장-도구변수
Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Testhttps://eml.berkeley.edu/~jmccrary/mccrary2006_DCdensity.pdf
Does Compulsory School Attendance Affect Schooling and Earnings?https://www.jstor.org/stable/2937954
https://github.com/partrita/causal-inference-in-python-code#12장-더-배울-내용
Causal Discovery Toolbox: Uncover causal relationships in Pythonhttps://arxiv.org/abs/1903.02278
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learninghttps://arxiv.org/abs/2211.12004
2022 Continuing Education Webcastshttps://www.aeaweb.org/conference/cont-ed/2022-webcasts
2019 AEA Continuing Education Webcastshttps://www.aeaweb.org/conference/cont-ed/2019-webcasts
Learning under Concept Drift: A Reviewhttps://ieeexplore.ieee.org/document/8496795
https://github.com/partrita/causal-inference-in-python-code#참고할-링크
https://github.com/CausalInferenceLab/causal-inference-with-causalMLhttps://github.com/CausalInferenceLab/causal-inference-with-causalML
https://github.com/CausalInferenceLab/causal-inference-lecture/tree/main/Lecture%20Noteshttps://github.com/CausalInferenceLab/causal-inference-lecture/tree/main/Lecture%20Notes
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