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 highlights the necessary shifts from traditional statistical analysis to causal analysis of multivariate data. It emphasizes the foundational assumptions of causal inferences, the language used to express these assumptions, and the conditional nature of causal and counterfactual claims, along with the methods developed to assess them. The discussion is grounded in a general theory of causation based on the Structural Causal Model (SCM), which integrates various approaches to causation and offers a coherent mathematical framework for analyzing causes and counterfactuals. The paper explores mathematical tools for addressing three types of causal queries: (1) the effects of potential interventions (causal effects or policy evaluation), (2) probabilities of counterfactuals (including "regret," "attribution," and "causes of effects"), and (3) direct and indirect effects (mediation). Additionally, it defines the formal and conceptual relationships between structural and potential-outcome frameworks, presenting tools for a combined analysis that leverages the strengths of both. These tools are illustrated through analyses of mediation, causes of effects, and probabilities of causation.

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.