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Title: GitHub - CausalInferenceLab/causal-inference-in-python-code: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다.

Open Graph Title: GitHub - CausalInferenceLab/causal-inference-in-python-code: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다.

X Title: GitHub - CausalInferenceLab/causal-inference-in-python-code: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다.

Description: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다. - CausalInferenceLab/causal-inference-in-python-code

Open Graph Description: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다. - CausalInferenceLab/causal-inference-in-python-code

X Description: "실무로 통하는 인과추론 with 파이썬" (한빛미디어, 2024) 소스코드 저장소입니다. - CausalInferenceLab/causal-inference-in-python-code

Opengraph URL: https://github.com/CausalInferenceLab/causal-inference-in-python-code

X: @github

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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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Causal Inference:The Mixtapehttps://www.scunning.com/mixtape.html
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A Randomized Assessment of Online Learninghttps://www.aeaweb.org/articles?id=10.1257/aer.p20161057
The Most Dangerous Equationhttps://www.americanscientist.org/article/the-most-dangerous-equation
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Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccinehttps://www.nejm.org/doi/full/10.1056/nejmoa2035389
Is It Live or Is It Internet? Experimental Estimates of the Effects of Online Instruction on Student Learninghttps://www.journals.uchicago.edu/doi/10.1086/669930
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Difference-in-differences with variation in treatment timinghttps://www.sciencedirect.com/science/article/abs/pii/S0304407621001445
Interpreting Ols Estimands When Treatment Effects are Heterogeneous: Smaller Groupshttps://direct.mit.edu/rest/article/104/3/501/97692/Interpreting-OLS-Estimands-When-Treatment-Effects
Contamination Bias in Linear Regressionshttps://arxiv.org/abs/2106.05024
On the Pooling of Time Series and Cross Section Datahttps://www.jstor.org/stable/1913646
Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Datahttp://robotics.stanford.edu/~ronnyk/2013-02CUPEDImprovingSensitivityOfControlledExperiments.pdf
https://patch-diff.githubusercontent.com/CausalInferenceLab/causal-inference-in-python-code#5장-성향점수
Estimating Treatment Effects with Causal Forests: An Applicationhttps://arxiv.org/abs/1902.07409
Why Propensity Scores Should Not Be Used for Matchinghttps://gking.harvard.edu/publications/why-Propensity-Scores-Should-Not-Be-Used-Formatching
A Contextual-Bandit Approach to Personalized News Article Recommendationhttps://arxiv.org/abs/1003.0146
An Introduction to the Augmented Inverse Propensity Weighted Estimatorhttps://www.law.berkeley.edu/files/AIPW(1).pdf
Comment: Performance of Double-Robust Estimators When “Inverse Probability” Weights Are Highly Variablehttps://arxiv.org/pdf/0804.2965.pdf
Causal inference with a continuous treatment and outcome: Alternative estimators for parametric dose-response functions with applicationshttps://www.proquest.com/openview/888e50198b2563222f8e2030e5a8c9e7/1?pq-origsite=gscholar&cbl=18750
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Nubank fklearnhttps://github.com/nubank/fklearn
Causal inference and uplift modeling a review of the literaturehttps://proceedings.mlr.press/v67/gutierrez17a/gutierrez17a.pdf
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationhttps://arxiv.org/abs/2211.01939
Intelligent Credit Limit Management in Consumer Loans Based on Causal Inferencehttps://arxiv.org/abs/2007.05188
Causal Classification: Treatment Effect Estimation vs. Outcome Predictionhttps://jmlr.org/papers/v23/19-480.html
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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
Nonparametric Estimation of Heterogeneous Treatment Effects:From Theory to Learning Algorithmshttps://proceedings.mlr.press/v130/curth21a/curth21a.pdf
Learning Representations for Counterfactual Inferencehttps://proceedings.mlr.press/v48/johansson16.pdf
https://patch-diff.githubusercontent.com/CausalInferenceLab/causal-inference-in-python-code#8장-이중차분법
Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvaniahttps://davidcard.berkeley.edu/papers/njmin-aer.pdf
When Should You Adjust Standard Errors for Clustering?https://academic.oup.com/qje/article/138/1/1/6750017
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controlshttps://www.tandfonline.com/doi/full/10.1080/01621459.2021.1920957
When Is Parallel Trends Sensitive to Functional Form?https://cowles.yale.edu/sites/default/files/2022-11/2010.04814.pdf
Difference-in-Differences Estimation with Spatial Spillovershttps://arxiv.org/abs/2105.03737
Causal Inference with Time-Series Cross-Sectional Data: A Reflectionhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3979613
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?https://imai.fas.harvard.edu/research/files/FEmatch.pdf
Doubly robust difference-in-differences estimatorshttps://www.sciencedirect.com/science/article/abs/pii/S0304407620301901
Difference-in-differences with variation in treatment timinghttps://www.sciencedirect.com/science/article/abs/pii/S0304407621001445
Higher Education Expansion, Labor Market, and Firm Productivity in Vietnamhttps://sites.google.com/view/khoavu-umn/job-market-paper
Estimating dynamic treatment effects in event studies with heterogeneous treatment effectshttps://www.sciencedirect.com/science/article/abs/pii/S030440762030378X
Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimatorshttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3906345
Difference-in-Differences with multiple time periodshttps://www.sciencedirect.com/science/article/abs/pii/S0304407620303948
https://patch-diff.githubusercontent.com/CausalInferenceLab/causal-inference-in-python-code#9장-통제집단합성법
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspectshttps://www.aeaweb.org/articles?id=10.1257/jel.20191450
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controlshttps://arxiv.org/abs/1712.09089
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://patch-diff.githubusercontent.com/CausalInferenceLab/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://patch-diff.githubusercontent.com/CausalInferenceLab/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://patch-diff.githubusercontent.com/CausalInferenceLab/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
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