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Causality is central to the understanding and use of data. Without an understanding of cause-effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients ?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters ; the assumptions necessary to estimate causal parameters in a variety of situations ; how to express those assumptions mathematically ; whether those assumptions have testable implications ; how to predict the effects of interventions ; and how to reason counterfactually.
These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law ; a brief introduction to probability and statistics is provided for the uninitiated ; and each chapter comes with study questions to reinforce the readers understanding.
Une révolution
Un grand pas pour l'humanité : Ce livre explique très clairement et simplement comment on peut extraire des données statistiques non pas seulement des informations synthétiques qui sont certes plus parlantes que les données détaillés, non pas seulement des corrélations mais aussi des informations de causalité.
A la fin du livre on se demande comment se fait il qu'il ait fallu attendre le 21ème siècle pour découvrir cela.
Un autre livre de J Pearl The book of why traite (excellemment) du même sujet sans utiliser quasiment aucune formule mathématique, mais si vos êtes à l'aise avec des équations (simples) utilisant des probabilités conditionnelles Causal Inference in statistics vous paraitra sans doute d'un accès plus évident car autant utiliser les bons outils pour appréhender rapidement les choses.