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Actual causality
Halpern J., The MIT Press, Cambridge, MA, 2016. 240 pp. Type: Book (978-0-262035-02-6)
Date Reviewed: Feb 28 2017

There are two types of causality according to Halpern: type causality (“Smoking causes cancer”) and actual causality (“Were the faulty brakes responsible for the accident?”). According to the author, “Type causality is typically forward-looking and used for prediction. Actual causality, in contrast, is more often backward-looking. With actual causality, we know the outcome, and we retrospectively ask why it occurred.” In this book, Halpern delves into the topic of actual causality.

Some of Halpern’s interesting viewpoints are brought out here, which might induce serious readers to explore the work in detail. The book is organized into eight chapters, the first one being “Introduction and Overview.”

In chapter 2, principles presumed to be governing causality are problematized by exploring the HP definition of causality (author’s prior work) with great detail. It concludes, “The definition given here seems somewhat closer to the intuitions and allows the consideration of probabilistic sufficient causality by putting a probability on contexts. This in turn makes it possible to bring out the connections between causality, normality and blame.”

“Graded Causation and Normality,” chapter 3, mentions (among other issues) the fact that “the motivation for putting the normality ordering on worlds rather than contexts was to solve problems that resulted from putting the normality ordering on contexts!”

In chapter 4, “The Art of Causal Modeling,” the author states:

Some have argued that causality should be an objective feature of the world. [...] In the context of HP approach, this would amount to designating one causal model as the right model. Indeed, in the spirit of the quote at the beginning of this chapter, I am not sure that there are any right models, but some models may be more useful, or better representations of reality than others. Moreover, even for a single situation, there may be many useful models.

Justification for models could not have been better explained.

Chapter 5 discusses “Complexity and Axiomatization.” The debate is triggered at the very beginning:

Is it plausible that people actually work with structural equations and (extended) causal models to evaluate actual causation? People are cognitively limited. If we represent the structural equations and the normality ordering in what is perhaps the most obvious way, the models quickly become large and complicated, even with a small number of variables.

“Responsibility and Blame” gets extensive coverage in chapter 6. Instead of attempting to relate whether or not an event caused another event, the notion of degree of responsibility is introduced. Here again, the anecdote in the title captures the essence: “I enjoyed the position I was in as a tennis player. I was to blame when I lost. I was to blame when I won. And I really like that, because I played soccer a lot too, and I couldn’t stand it when I had to blame it on the goalkeeper.”

Chapter 7 discusses “Explanation.” “The classical approaches to defining explanation in the philosophy literature, such as Hempel’s deductive-nomological model and Salmon’s statistical-relevance model, fail to exhibit the directionality intent in common explanations.” The limitations of deductive reasoning are brought out brilliantly through various examples here, by problematizing ontic reasoning or sets of premises that we usually take for granted.

Chapter 8, “Applying the Definitions,” concludes the discussion.

Overall, the book is interdisciplinary in its outlook, rich with examples, reflective of a canonical discourse, and promises to engage readers from a wide range of disciplines.

Reviewer:  CK Raju Review #: CR145083 (1705-0258)
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