Mathematics Causal reasoning / Taku Kanemoto

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Mathematics
Using Python and causal reasoning to improve the quality of decision-making Through the use of causal reasoning, this book presents solutions to the problems of verification of effects, such as "We don't know how effective each measure was," and the problems that frequently occur in the field of data analysis, such as "Correlation is strong, but causality is unknown." Furthermore, by learning from the basics of causal reasoning, in combination with machine learning and time series analysis, and even causal search, we can deal with a wide range of problems centered on causal reasoning. By doing so, we can significantly improve the impact of using data. In general, theoretical explanations are accompanied by many technical terms, long explanations, and complex codes, but this book removes such barriers and explains the theory in an easy-to-understand manner through specific examples and abundant illustrations. While keeping in mind that the minimum necessary code is included, we aim to ensure that it is applicable in practical applications. This approach allows readers to narrow the gap between theory and practice and to maximize the power of causal reasoning.