数理論理学

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.

2022.04.07

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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)

2022.04.06

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Protected: Basics of Statistical Causal Effects (3)Operating Variable Method and Summary

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2022.03.31

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

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

2022.03.30

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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

2022.03.29

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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...

2022.03.27

グラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Protected: Quasi-experimental design – how to derive causal relationships from observed data

How to verify causality for digital transformation, artificial intelligence , and machine learning tasks by first having the data for causal inference and then verifying causality from there.

2022.03.25

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Protected: Correlation, Causation and Relational Structure (2) Backdoor Criteria

Actual backdoor criteria for narrowing down variables to observe intervention effects in causal inference for digital transformation , artificial intelligence, and machine learning tasks

2022.03.23

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Discrepancy between correlation (regression coefficient) and causation (intervention effect)

Differences between regression coefficients and intervention effect values for digital transformation, artificial intelligence , and machine learning tasks.

2022.03.22

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Machine Learning Professional Series “Submodular Optimization and Machine Learning” reading notes

Machine Learning Professional Series "Submodular Optimization and Machine Learning" reading notes Submodular f...

2022.03.21

Symbolic Logicアルゴリズム:Algorithmsグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics