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.

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)

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.

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)

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)

We interpret the probability rule of the CSL collapse theory to mean to mean that the scalar field which causes collapse is the gravitational curvature scalar with two sources, the expectation value of the mass density (smeared over the GRW scale a) and a white noise fluctuating source. We examine two models of the fluctuating source, monopole fluctuations and dipole fluctuations, and show that these correspond to two well-known CSL models. We relate the two GRW parameters of CSL to fundamental (...) constants, and we explain the energy increase of particles due to collapse as arising from the loss of vacuum gravitational energy. (shrink)

We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.

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)

A key motivation for Universal Grammar (UG) is developmental: UG can help children acquire the linguistic knowledge that they do as quickly as they do from the data that's available to them. Some of the most fruitful recent work in language acquisition has combined ideas about different hypothesis space building blocks with domain‐general statistical learning. Statistical learning can then provide a way to help navigate the hypothesis space in order to converge on the correct hypothesis. Reinforcement learning is a principled (...) way to update the probability of a categorical option that's in competition with other categorical options. There are two prominent options for linking theory theoretical representations: the Uniformity of Theta Assignment Hypothesis (UTAH) and the relativized form of that theory, relativized UTAH. The learning strategy of Pearl and Sprouse shares the intuition with subjacency that there's a local structural anomaly when syntactic islands occur. (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.

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)

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)

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)

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 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)