Pearl, Judea and Mackenzie, Dana: The book of why: the new science of cause and effect (2018)
'The Book of Why' by Judea Pearl and Dana Mackenzie is for you if you are engaged in the pursuit of Data Science, artificial intelligence, cognition, deep learning; or curious about "correlation is not causation" the 'ladder of causation' confounding bias, Bayesian rule and "do" calculus; or wondering whether a robot with real humanlike intelligence would be created if causality were integrated into artificial intelligence; and are interested in exploring whether counterfactuals and mediation can lead to better prediction, explanations, and diagnosis of treatments and health care strategies. The authors sum up the message of their book when they say that: "Data do not understand causes and effects, humans do" The advice to big data and model free enthusiasts is that you may be able to tease out all the information from data but it will 'never' answer even a simple question of causation, "What is the relative importance of various causes?" In doing so, the authors take issue with the false belief that 'the answers to all scientific problems reside in the data, to be unveiled through clever data mining tricks' Although the data centric belief still prevails under such umbrellas as 'data science' 'data economy' and 'data centric intelligence' they posit that data alone cannot make up for lack of scientific knowledge. In recognizing the 'dumbness' of data, the authors argue for a shift from data driven science to cause effect relationships. They call it the 'causal revolution' which is rooted in the rigour of 'calculus of causation' The calculus of causation consists of two languages: causal diagrams, to express what we know, and a symbolic language, resembling algebra, to express what we want to know. One of the 'crowning achievements' of the 'Causal Revolution' they say, has been to explain how to predict the effects of an intervention without actually enacting it. Causal reasoning, when it involves retrospective thinking of scientific questions, is termed counterfactuals. Another gem of the causal revolution, Pearl says, is that in many cases we can use algorithms to emulate human retrospective thinking and produce answers to the observed counterfactual world. Counterfactual reasoning, which deals with what ifs, is seen as the building blocks of moral behaviour, as well as scientific thought. The ability to reflect on one's past actions and envision alternative scenarios is the basis of free will and social responsibility. Pearl gives us an insight into his commitment to the 'Causal revolution' when he says that his departure on causal inference from his AI colleagues resides in his view of the AI world, that "you do not really understand a topic until you can teach it to a mechanical robot" Moreover, this view resides in his deep conviction that "language shapes our thoughts. You cannot answer a question that you have no words for" For Pearl, a major challenge remains, 'how can machines acquire causal knowledge? Deep learning and data mining algorithms devoid of causal models can only fit a function to data, can only interpret data through a causal 'model blind' lens, forgetting that human intuition and human intelligence are rooted in causal and not statistical logic. Consequently these algorithms lack adaptability to deal with prediction in the uncertain world. Pearl takes the reader on a historical journey beginning with the foundation of the 'holy grail' of data and objectivity that is rooted in the 1834 founding charter of the Statistical Society of London. This said that 'data were to receive priority in all cases over opinions and interpretations. Data are objective; opinions are subjective' In the course of the journey, we learn about Francis Galton's pursuit of the inheritance of stature and genetic traits, Karl Pearson's passion for correlation and causality free science, Sewall Wright's construction of a path to the bridge between causality and Karamjit S. Gill editoraisoc@yahoo.co.uk
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