The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at (...) a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causality was not just a victory for common sense, but a revolution in the study of the world. (shrink)

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.

We show in this paper that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a (...) belief set. We also show that a simple modification to the AGM framework can allow belief revision to be a function of epistemic states. We establish a model-based representation theorem which characterizes the proposed postulates and constrains, in turn, the way in which entrenchment orderings may be transformed under iterated belief revision. (shrink)

This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...) which is syntactically derived from the knowledge base. This ordering accounts for rule interactions, respects specificity considerations and facilitates the construction of coherent states of beliefs. Practical algorithms are developed and analyzed for testing consistency, computing rule ordering, and answering queries. Imprecise observations are incorporated using qualitative versions of Jeffrey's rule and Bayesian updating, with the result that coherent belief revision is embodied naturally and tractably. Finally, causal rules are interpreted as imposing Markovian conditions that further constrain world rankings to reflect the modularity of causal organizations. These constraints are shown to facilitate reasoning about causal projections, explanations, actions and change. (shrink)

This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback-less) models are considered. Composition and effectiveness also hold in Lewis's closest-world semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not (...) in Lewis's closest-world semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models. (shrink)

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature.

Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the “possible worlds” account of counterfactuals, this “structural” model enjoys the advantages of representational economy, algorithmic simplicity, and conceptual clarity. This introduction traces the emergence of the structural model and gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.

According to common judicial standard, judgment in favor ofplaintiff should be made if and only if it is more probable than not thatthe defendant''s action was the cause for the plaintiff''s damage (or death). This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient)causation can be learned from statistical data, and (...) shows how data fromboth experimental and nonexperimental studies can be combined to yieldinformation that neither study alone can provide. Finally, we show thatnecessity and sufficiency are two independent aspects of causation, andthat both should be invoked in the construction of causal explanations for specific scenarios. (shrink)

I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis (...) of fitting and interpreting, guided by the Logic of Causation. (shrink)

This paper provides empirical interpretation of the do(x)do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x)do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally (...) eligible to receive the do(x)do(x) operator and to produce useful information for decision makers. (shrink)

In A Question of Time, Joel Pearl offers a new reading of the foundations of psychoanalytic thought, indicating the presence of an essential lacuna that has been integral to psychoanalysis since its inception. Pearl returns to the moment in which psychoanalysis was born, demonstrating how Freud had overlooked one of the most principal issues pertinent to his method: the question of time. The book shows that it is no coincidence that Freud had never methodically and thoroughly discussed time and that (...) the metaphysical assumption of linear time lies at the very heart of Freudian psychoanalysis. Pearl¿s critical reading of Freud develops through an original dialogue that he creates with the philosophy of Martin Heidegger and, specifically, with the German philosopher¿s notion of temporality. Pearl traces the encounter between Freud and Heidegger by observing the common inspiration shaping their thinking: philosopher Franz Brentano, who taught both Freud and Edmund Husserl, Heidegger¿s mentor. The book travels down an alternate path, one overlooked by Freudian thought ¿ a path leading from Brentano, through Husserl and onto Heidegger¿s notion of time, which is founded on the ecstatic¿ interrelation of past, present and future. Dr. Joel Pearl specializes in phenomenology and psychoanalysis on which he has published several articles. He teaches philosophy at the Academic College of Communication, Rishon LeZion, Israel. In addition, Pearl is a dynamic psychotherapist and a member of The Tel-Aviv Institute for Contemporary Psychoanalysis. (shrink)

UCLA Cognitive Systems Laboratory, Technical Report (R-321), June 2005. In Proceedings of International Joint Conference on Artificial Intelligen ce, Edinburgh, Scotland, August 2005.

Heinrich Popitz’s Phenomena of Power aims to uncover power as “a universal component in the genesis and operation of human societies”. In order to uncover this “universal” concept of power, Popitz employs Husserl’s method of the “imaginative variation” [Phantasievariation]. Yet, contrary to phenomenology’s traditionally descriptive posture, Phenomena of Power’s project is at once descriptive and normative—seeking not only to describe power, but to also describe the way in which power can be remade. In the present paper it is argued that (...) this normative component of Popitz’s project offers the burgeoning field of critical phenomenology an illustration of the way in which Husserl’s “imaginative variation” might be employed not only as a descriptive tool of pure essences, but also as an instrument in the refashioning of social reality. (shrink)

Quentin Meillassoux’s 2006 After Finitude offered a sharp critique of the phenomenological project, charging that phenomenology was one of the “two principal media” of correlationism—ultimately reducible to an “extreme idealism.” Meillassoux grounds this accusation in an account of givenness that presupposes that “every variety of givenness” finds its genesis within the positing of the subject. However, this critique fails to hit its mark precisely because it presupposes an account of intuitive givenness that is entirely foreign to the phenomenological project. Quite (...) against Meillassoux’s conflation of givenness, the world-for-us, and the positing subject—the very centre of the phenomenological project is the recognition that intuitive givenness cannot be reduced to the constructive activity of the subject. Givenness is marked by a heterogeneity; givenness refers to what is given to us, not to what emerges from us. (shrink)

This note aims to assist applied econometricians in understanding the tools of causal inference and to extend those discussed in Nick Huntington-Klein's review of The Book of Why.

I thank Dr. Woodward for his illuminating review of my book Causality, for explicating so clearly the basic contributions of the book, and for giving me the opportunity to further clarify some aspects of the do-calculus, specifically those that pertain to the notion of intervention.

Since Husserl’s phenomenological analyses of the living body (Leib) in Ideas II, the subjective experience of the body, what later French thinkers will name the flesh, has been particularly marked by its capacity for action—its potency. This privileging of the acting flesh, the potent organ, is echoed throughout the subsequent phenomenological tradition. For this tradition, from de Biran and Husserl, to Merleau‑Ponty and Henry, the flesh is distinguished from the mere body (Körper) by its unique capacity to act. For the (...) later French tradition to be flesh is to be will, and to be will is to be action. Impotence (impuissance) appears as external resistance. In the present investigation, I challenge this trajectory of the flesh through a phenomenological investigation of the unique phenomenon of acute sexual impotence. In this way, it will be shown that l’impuissance (impotence) can be located not only in the objectivity of opposition within the world, on the organic or objective body (le corps, Körper), but within the immanence of the flesh (la chair, Leib) itself. Indeed, the experience of incarnate l’impuissance provides a unique phenomenological access to the incomplete synthesis of the carnal flesh—a synthesis that remains, by phenomenological necessity, run through with ruptures and gaps that function as its constitutive excess. (shrink)