最適化:Optimization

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Protected: Effectiveness of “nursery development” verified by difference in difference

Actual causal inference using the difference-in-differences method, one of the causal inference methods relationship between daycare center development and female employment rate
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Protected: Estimating Bunt Effects Using Propensity Scores

Estimating baseball bunt effects using propensity scores as an application of causal inference for digital transformation, artificial intelligence, and machine learning tasks.
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Protected: Application of causal effect estimation – Causal and adjustment effects of commercial contact

Specific applications of statistical causal inference used in digital transformation, artificial intelligence, and machine learning tasks (causal and adjusted effects of CM contact using average treatment effect ATE and average treatment effect ATT in treatment groups)
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Iwanami Data Science – The World of Bayesian Modeling Reading Notes

Iwanami Data Science - The World of Bayesian Modeling Reading Notes Memo for reading "Iwanami Data Science: Th...
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From Inductive logic Programming 2016 Proceedings

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
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Protected: Basics of Statistical Causal Effects (3)Operating Variable Method and Summary

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Protected: Basics of Statistical Causal Effects (2)Methods Using Regression Models and Matching and Stratified Analysis

Regression modeling methods for statistical causal inference utilized in digital transformation, artificial intelligence, and machine learning tasks; causal effect estimation using matching and stratified analysis methods
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Protected: Basics of Statistical Causal Effects (1)Definition of Causal Effects Based on the Rubin Effect Model

Definition of causal effects and estimation of statistical causal effects (ATT, ATU, ATE) based on the Rubin effect model used for digital transformation, artificial intelligence and machine learning tasks
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Machine Learning Professional Series – Gaussian Processes and Machine Learning Reading Notes

Summary A Gaussian Process (GP) is a nonparametric regression and classification method based on probability th...
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From Inductive logic Programming 2012 Proceedings

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
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