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)

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. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...) models. (shrink)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

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

This article illustrates how a collaborative research process can successfully engage an underserved minority community to address health disparities. Pacific Islanders, including the Marshallese, are one of the fastest growing US populations. They face significant health disparities, including extremely high rates of type 2 diabetes. This article describes the engagement process of designing patient‐centered outcomes research with Marshallese stakeholders, highlighting the specific influences of their input on a randomized control trial to address diabetes. Over 18 months, an interdisciplinary research team (...) used community‐based participatory principles to conduct patient‐engaged outcomes research that involved 31 stakeholders in all aspects of research design, from defining the research question to making decisions about budgets and staffing. This required academic researcher flexibility, but yielded a design linking scientific methodology with community wisdom. (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 support Jaswal & Akhtar's interrogation of social motivational accounts of autism and discuss two sources of bias that contribute to how others construe autistic people's communications: an experience-based bias that limits our ability to discern the speaker's action as communicative and a prejudice against the credibility of certain speakers that limits a listener's willingness to believe their testimony.

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 difficultiesn in the traditional account.

The present study examines the fortuna in the Middle Ages of the Galenic work in fourteen books on Therapeutics, the De Methodo medendi or Therapeutica, known also in the medieval centuries under the titles Ars magna, Megategni and De ingenio sanitatis. After tracing the history of the translations of this text into Latin from the Arabic and the Greek, the study focuses on the De Methodo medendi's place in the medical curricula of the medieval universities. The essay closes with an (...) extensive listing of the medieval manuscript copies of this valuable Galenic treatise. (shrink)

We extend Tomasello's discussion of children's developing sense of obligation to testimonial learning. First, we review a battery of behaviors in testimonial exchanges that parallel those described by Tomasello. Second, we explore the variable ways in which children hold others accountable, suggestive that children's evaluations of moral and epistemic responsibilities in joint collaborative activities are distinct.

The following Addenda and some Corrigenda were derived for the most part from manuscripts examined in the summer of 1964 and in the spring and summer of 1965, 1967 at Bologna University ; London, British Museum ; Munich, Bayerische Staats-Bibliothek, Codex Latinus Monacensis ; Orlèans; Oxford, the Bodleian ; and Merton College ; Paris, Bibliotheque nationale , and University ; Tours; Vatican, Bibliotheca Apostolica Vaticana ; and Vienna, National-Bibliothek . A number are noted from other sources, namely Silvestre , Wickersheimer (...) , et alia. They supplement the late Lynn Thorndike's “Additional Addenda et Corrigenda” in Speculum , and are here offered for the convenience of users of the Catalogue of Incipits. jQuery.click { event.preventDefault(); }). (shrink)

Effective stress management strategies correlate with improved academic performance in college students, yet inconsistent findings in existing research warrant further investigation. This study explored the intricate interplay between stress levels, coping strategies, and academic achievement in HyFlex learningenvironments. A stratified random sample of 111 students from five specializations within the Bachelor of Secondary Education program participated. Utilizing a descriptive-correlational design, data were collected through validated self-report questionnaires and a weighted general average. Subsequent descriptive statistics and bivariate correlation analysis revealed moderate (...) stress levels among participants who preferred task-oriented coping strategies. Interestingly, a significant positive association was observed between stress levels and academic achievement and emotion-oriented coping strategies, while a negative correlation emerged between stress and task-oriented coping strategies. Avoidance-oriented coping strategies, conversely, displayed a significant negative association with academic success. These findings underscore the crucial role of educational institutions in providing robust support mechanisms for stress management and cultivating a task-focused learning environment to optimize student well-being and academic performance within the HyFlex model. (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)

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.

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)

Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest (...) in causation will also profit from reading Pearl’s book, especially the material in chapters 7, 9, and 10, which is self-contained and less technical than other parts of the book. The present review is aimed primarily at readers of the second type. (shrink)

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)

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. (...) 1. Introduction2. Causal models and the definition of actual causality: a review2.1Causal models2.2Syntax and semantics2.3The definition of cause3. Explanation: the basic definition4. Partial explanations and explanatory power5. The general definition6. DiscussionAppendix: the formal definition of causality. (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.

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)

We propose new definitions of 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.

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.