Judea Pearl Knihy

Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality.

The book delves into the evolution of causation from a vague concept to a robust mathematical theory, highlighting its applications across various disciplines such as statistics, AI, and economics. Judea Pearl integrates different approaches to causation, providing accessible mathematical tools for exploring causal relationships and statistical associations. This revised edition addresses complex issues and recent advancements, making it valuable for students and professionals alike. Pearl's significant contributions to AI research are also recognized, enhancing the book's credibility in the field.

'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer and carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl's discoveries have enabled machines to think better, The Book of Why explains how we can think better.

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1

Wszyscy wiemy, że pianie koguta o świcie, nie wywołuje wschodu słońca. Jednocześnie nie mamy wątpliwości, że użycie włącznika spowoduje zapalenie lub zgaszenie światła. Skąd zatem pewność, że jedno zdarzenie spowodowało drugie? Przyczynowość jest jedną z najszerzej dyskutowanych i najtrudniejszych do wykazania kategorii w nauce i medycynie. Rewolucja Przyczynowa, zainicjowana przez Judeę Pearla i innych badaczy, położyła kres wiekowi niejasności pojęciowych i oparła przyczynowość na solidnej podstawie naukowej. Dzieło Pearla i Mackenziego zawiera historię samej idei, a także dostarcza narzędzi niezbędnych do oceny, czego może - lub nie - dokonać Big Data. Autorzy tłumaczą, na czym polega drabina przyczynowości i opierając się na wielu przykładach z życia, ukazują istotę ludzkiej myśli oraz klucz do sztucznej inteligencji. Każdy, kto pragnie zrozumieć jedno lub drugie, powinien przeczytać książkę Przyczyny i skutki.