The book discusses recent developments in philosophy with regards to how historical events can be explained causally and introduces perspectives from the philosophy of science into the philosophy of history. The book covers the main approaches to causal explanation in history and makes clear the main issues and concepts that are associated with such explanations. For example, the book discusses historical contingency and narratives, among other issues. The book is directed to philosophers of science, philosophers of history, historians, (...) historically oriented social scientists, and everyone who is interested in how we make sense of the past. (shrink)

There is a major debate as to whether there are non-causal mathematical explanations of physical facts that show how the facts under question arise from a degree of mathematical necessity considered stronger than that of contingent causal laws. We focus on Marc Lange’s account of distinctively mathematical explanations to argue that purported mathematical explanations are essentially causal explanations in disguise and are no different from ordinary applications of mathematics. This is because these explanations work not by appealing (...) to what the world must be like as a matter of mathematical necessity but by appealing to various contingent causal facts. (shrink)

To give a causal explanation is to give information about causal history. But a vast amount of causal history lies behind anything that happens, far too much to be included in any intelligible explanation. This is the Problem of Limitation for explanatory information. To cope with this problem, explanations must select for what is relevant to and adequate for answering particular inquiries. In the present paper this idea is used in order to distinguish two kinds (...) of causal explanation, on the grounds of systematic differences in their conditions of relevance and adequacy. It is further argued that these two forms of causal explanation are interdependent and their interaction provides an instrument through which causal knowledge is acquired and enhanced. What we understand causation in the world to be is neither unconditioned regularity, nor counterfactual dependence, but the sum of correct answers to explanatory inquiries of these two interdependent kinds. (shrink)

How do cognitive neuroscientists explain phenomena like memory or language processing? This book examines the different kinds of experiments and manipulative research strategies involved in understanding and eventually explaining such phenomena. Against this background, it evaluates contemporary accounts of scientific explanation, specifically the mechanistic and interventionist accounts, and finds them to be crucially incomplete. Besides, mechanisms and interventions cannot actually be combined in the way usually done in the literature. This book offers solutions to both these problems based on (...) insights from experimental practice. It defends a new reading of the interventionist account, highlights the importance of non-interventionist studies for scientific inquiry, and supplies a taxonomy of experiments that makes it easy to see how the gaps in contemporary accounts of scientific explanation can be filled. The book concludes that a truly empirically adequate philosophy of science must take into account a much wider range of experimental research than has been done to date. With the taxonomy provided, this book serves a stepping-stone leading into a new era of philosophy of science—for cognitive neuroscience and beyond. (shrink)

Not all scientific explanations work by describing causal connections between events or the world's overall causal structure. In addition, mathematicians regard some proofs as explaining why the theorems being proved do in fact hold. This book proposes new philosophical accounts of many kinds of non-causal explanations in science and mathematics.

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

It is widely believed that many of the competing accounts of scientific explanation have ramifications which are relevant to the scientific realism debate. I claim that the two issues are orthogonal. For definiteness, I consider Cartwright's argument that causal explanations secure belief in theoretical entities. In Section I, van Fraassen's anti-realism is reviewed; I argue that this anti-realism is, prima facie, consistent with a causal account of explanation. Section II reviews Cartwright's arguments. In Section III, it (...) is argued that causal explanations do not license the sort of inferences to theoretical entities that would embarass the anti-realist. Section IV examines the epistemic commitments involved in accepting a causal explanation. Section V presents my conclusions: contra Cartwright, the anti-realist may incorporate a causal account of explanation into his vision of science in an entirely natural way. (shrink)

Although standard Machine Learning models are optimized for making predictions about observations, more and more they are used for making predictions about the results of actions. An important goal of Explainable Artificial Intelligence (XAI) is to compensate for this mismatch by offering explanations about the predictions of an ML-model which ensure that they are reliably action-guiding. As action-guiding explanations are causal explanations, the literature on this topic is starting to embrace insights from the literature on causal models. Here (...) I take a step further down this path by formally defining the causal notions of sufficient explanations and counterfactual explanations. I show how these notions relate to (and improve upon) existing work, and motivate their adequacy by illustrating how different explanations are action-guiding under different circum- stances. Moreover, this work is the first to offer a formal definition of actual causation that is founded entirely in action-guiding explanations. Although the definitions are motivated by a focus on XAI, the analysis of causal explanation and actual causation applies in general. I also touch upon the significance of this work for fairness in AI by showing how actual causation can be used to improve the idea of path-specific counterfactual fairness. (shrink)

This work consists of two parts. Part I will be a contribution to a philo- sophical discussion of the nature of causal explanation. It will present my contrastive counterfactual theory of causal explanation and show how it can be used to deal with a number of problems facing theories of causal explanation. Part II is a contribution to a discussion of the na- ture of interest explanation in social studies of science. The aim (...) is to help to resolve some controversies concerning interest explanation by explicating the concept of interest and its explanatory uses by using the account of explanation developed in Part I. (shrink)

The existence of causal explanations in science has been an issue of interest in Western philosophy from its very beginnings. That is the reason this work, following an idea of Mario Bunge, makes a historical review of this matter. The modern treatment of this subject takes place since the postulation by Popper and Hempel of the D-N model of scientific explanation, whose viability is scrutinized here from different points of view in the current philosophy of science. The main (...) object of this paper is to present two arguments against the possibility of causal explanations in theoretical physics. The first one concerns the existence, in certain cases, of inter-theoretical incompatibilities, and the second refers to the need to resort, in other cases, to concatenations of laws of different theories and disciplines. The final conclusion will be the defence of a form of theoretical explanation, which follows the Popper-Hempel model, but devoid of any ontological and metaphysical connotations. (shrink)

_This paper deals with the interrelationship between causal explanation and methodology in a relatively young discipline in biology: epigenetics. Based on cases from molecular and ecological epigenetics, I show that James Woodward’s interventionist account of causation captures essential features about how epigeneticists using highly diverse methods, i.e. laboratory experiments and purely observational studies, think about causal explanation. I argue that interventionism thus qualifies as a useful unifying explanatory approach when it comes to cross-methodological research efforts: It (...) can act as a guiding rationale to link causal models in molecular biology with statistical models derived from observational data analysis and to identify test-criteria for reciprocal transparent studies in different fields of research, which is a shared issue across the sciences.___. (shrink)

The periodic table represents and organizes all known chemical elements on the basis of their properties. While the importance of this table in chemistry is uncontroversial, the role that it plays in scientific reasoning remains heavily disputed. Many philosophers deny the explanatory role of the table and insist that it is “merely” classificatory (Shapere, in F. Suppe (Ed.) The structure of scientific theories, University of Illinois Press, Illinois, 1977; Scerri in Erkenntnis 47:229–243, 1997). In particular, it has been claimed that (...) the table does not figure in causal explanation because it “does not reveal causal structure” (Woody in Science after the practice turn in the philosophy, history, and social studies of science, Routledge Taylor & Francis Group, New York, 2014). This paper provides an analysis of what it means to say that a scientific figure reveals causal structure and it argues that the modern periodic table does just this. It also clarifies why these “merely” classificatory claims have seemed so compelling–this is because these claims often focus on the earliest periodic tables, which lack the causal structure present in modern versions. (shrink)

In the recent literature on causal and non-causal scientific explanations, there is an intuitive assumption according to which an explanation is non-causal by virtue of being abstract. In this context, to be ‘abstract’ means that the explanans in question leaves out many or almost all causal microphysical details of the target system. After motivating this assumption, we argue that the abstractness assumption, in placing the abstract and the causal character of an explanation in (...) tension, is misguided in ways that are independent of which view of causation or causal explanation one takes to be most accurate. On major accounts of causation, as well as on major accounts of causal explanation, the abstractness of an explanation is not sufficient for it being non-causal. That is, explanations are not non-causal by dint of being abstract. (shrink)

Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent (...) to their consequents. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)

For the framework of event causation—i.e. the framework according to which causation is a relation between events—absences or omissions pose a problem. Absences, it is generally agreed, are not events; so, under the framework of event causation, they cannot be causally related. But, as a matter of fact, absences are often taken to be causes or effects. The problem of absence causation is thus how to make sense of causation that apparently involves absences as causes or effects. In an influential (...) paper, Helen Beebee offers a partial solution to the problem by giving an account of causation by absence. I argue that Beebee's account can be extended to cover causation of absence as well. More importantly, I argue that the extended Beebeeian account calls for a major modification to David Lewis's theory of causal explanation, usually taken as standard. Compared to the standard theory, the.. (shrink)

It is widely held that it is difficult, if not impossible, to apply causal theory to the domain of quantum mechanics. However, there are several recent scientific explanations that appeal crucially to quantum processes, and which are most naturally construed as causal explanations. They come from two relatively new fields: quantum biology and quantum technology. We focus on two examples, the explanation for the optical interferometer LIGO and the explanation for the avian magneto-compass. We analyse the (...) explanation for the avian magneto-compass from the perspective of Woodward's interventionist theory and provide a causal model. Furthermore, we show how worries expressed by Woodward about quantum causation are circumvented in these cases, concluding that these kinds of explanations are most naturally construed as causal. (shrink)

This paper considers the problem of causal explanation in classical and statistical thermodynamics. It is argued that the irreversibility of macroscopic processes is explained in both formulations of thermodynamics in a teleological way that appeals to entropic or probabilistic consequences rather than to efficient-causal, antecedental conditions. This explanatory structure of thermodynamics is not taken to imply a teleological orientation to macroscopic processes themselves, but to reflect simply the epistemological limitations of this science, wherein consequences of heat-work asymmetries (...) are either macroscopically measurable (entropy) or calculable (probabilities), while efficient-causal relationships are obscure or indeterminable. (shrink)

Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive (...) class='Hi'>causal explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

This paper argues against the view that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, a distortion that can nevertheless successfully represent causal relations.

This paper deals with the interrelationship between causal explanation and methodology in a relatively young discipline in biology: epigenetics. Based on cases from molecular and ecological epigenetics, I show that James Woodward’s interventionist account of causation captures essential features about how epigeneticists using highly diverse methods, i.e. laboratory experiments and purely observational studies, think about causal explanation. I argue that interventionism thus qualifies as a useful unifying explanatory approach when it comes to cross-methodological research efforts. It (...) can act as a guiding rationale (i) to link causal models in molecular biology with statistical models derived from observational data analysis and (ii) to identify test-criteria for reciprocal transparent studies in different fields of research, which is a shared issue across the sciences.Este artículo trata de la relación entre explicaciones causales y metodología en una disciplina biológica relativamente joven, la epigenética. Basándome en casos de la epigenética molecular y ecológica, muestro que la concepción intervencionista de la causalidad desarrollada por James Woodward capta algunos rasgos esenciales del modo en que los epigenetistas conciben la explicación causal usando métodos sumamente diversos: e.g., experimentos de laboratorio o estudios observacionales. Defiendo que el intervencionismo es útil como aproximación unificadora a la explicación cuando se trata de empresas investigadoras transdisciplinares. Puede servir como guía para (i) conectar los modelos causales en biología molecular con los modelos estadísticos derivados del análisis de datos observacionales y (ii) para identificar criterios de prueba para estudios recíprocos en diferentes ámbitos de investigación, un problema de interés común en diferentes ciencias. (shrink)

The humanistic disciplines aim to offer explanations of a wide variety of phenomena. Philosophical theories of explanation have focused mostly on explanations in the natural sciences; a much discussed theory of explanation is the causal theory of explanation. Recently it has come to be recognized that the sciences sometimes offer respectable explanations that are non-causal. This paper broadens the discussion by discussing explanations that are offered in the fields of history, linguistics, literary theory, and archaeology (...) that do not seem to fit the causal theory of explanation. We conducted an exploratory survey in acclaimed humanities textbooks to find explicitly so-called explanations and analyze their nature. The survey suggests that non-causal explanations are an integral part of the humanities and that they are of distinct kinds. This paper describes three kinds that are suggested by our survey: teleological, formal, and normative explanations. We suggest that such humanistic explanations strengthen the case for explanatory pluralism. (shrink)

It seems plausible to suppose that (a) the vocabulary of action is distinct from and irreducible to that of mere movement, And (b) the causal laws of the natural sciences are couched solely in terms of the latter vocabulary. From these two suppositions, The falsehood of determinism has sometimes been said to follow. I argue that whether this does follow depends on our conception of causal explanation; on the interpretation of this concept that seems to me the (...) most interesting, The falsehood of determinism follows only given the supplementary premise that there are no correlations between the relevant movement-Descriptions and action-Descriptions. But those who propose the two-Vocabularies argument have not shown the impossibility of establishing these correlations, And so have not proven determinism false. (shrink)

Carl Hempel argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon and Richard Jeffrey argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and (...) defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

Most discussions of causal explanations of behavior focus on the problem of whether it makes sense to regard reasons as causes of human behavior, whether there can be laws connecting reasons with behavior, and the like. This essay discusses explanations of human behavior that do not appeal to reasons. Such explanations can be found in several areas of the social sciences. Moreover, these explanations are both causal and non-reductionist. Historical linguists, for example, offer causal explanations of changes (...) in how words are pronouncedand linguistic change in generalwithout appealing to human intentions. I use examples from linguistics, anthropology, and evolutionary psychology to discuss the importance of this sort of explanation and to examine its compatibility with recent philosophical accounts of causation. (shrink)

In this paper, we present an approach to commonsense causal explanation of stories that can be used for automatically determining the liable party in legal case descriptions. The approach is based on, a core ontology for law that takes a commonsense perspective. Aside from our thesis that in the legal domain many terms still have a strong commonsense flavour, the descriptions of events in legal cases, as e.g. presented at judicial trials, are cast in commonsense terms as well. (...) We present design principles for representing commonsense causation, and describe a process-based approach to automatic identification of causal relations in stories, which are described in terms of the core ontology. The resulting causal explanation forms a necessary condition for determining the liability and responsibility of agents that play a role in the case. We describe the basic architecture and working of, the demonstrator we are constructing to test the validity of our process oriented view on commonsense causation. This view holds that causal relations are in fact abstractions constructed on the basis of our commonsense understanding of physical and mental processes. (shrink)

In previous work I have argued that talk about negative events should not be taken at face value: typically, what we are inclined to think of as a negative event (John’s failure to go jogging) is just an ordinary, positive event (his going to the movie instead); it is a positive event under a negative description. Here I consider more closely the difficulties that arise in those cases where no positive event seems available to do the job, as with putative (...) cases of causation by omission. In particular, I elaborate on Helen Beebee’s idea that not all causal explanations are reports of causation. When we mention John’s failure to turn off the gas as an explanans of why there was an explosion, we do not say what caused the explosion. We do not mention any of the relevant causes. We just remark that one sort of event that was supposed to occur, and whose occurrence would have prevented the explosion, did not in fact occur. (shrink)

It is observed that in ordinary everyday causal explanations often just one causal factor is mentioned. One causal factor carries the explanatory burden, even if there are several causal factors that are responsible for the event to be explained. This paper deals with the question of how to account for this explanatory selection. I argue for a pragmatic stance towards explanation, that we must attend to the question–answer situation as a whole and the context of (...) the explanation. The context of an explanation includes the inquirer's and the explainer's beliefs and presuppositions relevant for the explanation-seeking question, and these are encoded in a reference class. Furthermore I argue that the explanation-giving answer contains an implicit counterfactual claim, the explanation-giving counterfactual. The solution to the problem of explanatory selection is to be found in the presuppositions encoded by the reference class and the eg-counterfactual. In short we select as explanatory that factor which, together with the presupposition encoded in the reference class we believe will make the eg-counterfactual true. (shrink)

Recent philosophical work has explored the distinction between causal and non-causal forms of explanation. In this literature, topological explanation is viewed as a clear example of the non-causal variety–it is claimed that topology lacks temporal information, which is necessary for causal structure. This paper explores the distinction between topological and causal forms of explanation and argues that this distinction is not as clear cut as the literature suggests. One reason for this is (...) that some explanations involve both topological and causal information. In these “borderline” cases scientists explain some outcome by appealing to the causal topology of the system of interest. These cases help clarify a type of topological explanation that is genuinely causal, but that differs from standard topological and interventionist accounts of explanation. (shrink)

Philosophers have proposed many alleged examples of non-causal explanations of particular events. I discuss several well-known examples and argue that they fail to be non-causal. 1 Questions2 Preliminaries3 Explanations That Cite Causally Inert Entities4 Explanations That Merely Cite Laws I5 Stellar Collapse6 Explanations That Merely Cite Laws II7 A Final Example8 Conclusion.

This book aims to answer two main questions about Aristotle’s theory of causality and causal explanation, especially in relation to natural science: (1) How does he answer the main philosophical questions about causes to which he thinks his predecessors’ answers are flawed? (2) How do his answers bear on the main questions we confront in thinking about causality in general? The texts that deal with causality directly are analyzed against the background of his criticisms of his predecessors and (...) his broader views about explanation, highlighting their theoretically important commitments. This allows us to reveal the conceptual architecture of his theory. The basic theory is then applied to two questions about causality, one metaphysical and one epistemological. The metaphysical question is how to account for the relation between causes and what they cause, in light of a fundamental tension between different desiderata for causal explanations. Aristotle’s answer is pluralistic: he does not think that there is one metaphysical relationship between causes and what they cause even within a given mode of causality. The epistemological question asks how we grasp causes, and grasp them in a way that yields scientific understanding. Aristotle’s answer is again a pluralist one: metaphysically different phenomena demand different cognitive resources, and some are more accessible than others. Nevertheless, there is a discernible path from simpler to more sophisticated types of cognitive grasp on causality, in such a way that Aristotle can plausibly satisfy the epistemological constraints he thinks his predecessors cannot. Aristotle thus aims to provide a non-reductionist account of causality that still leaves room for a substantive conception of understanding in natural science. (shrink)

This paper characterizes the role of the experimenter in causal explanations of laboratory phenomena. Causal explanation rests on appeals to the experimenter's efficacy as a causal agent. I contrast "demographic" and "genetic" explanations of stochastic outcomes in a set of competition experiments in ecology. The demographic view ascribes causes to the experimenter's agency in setting up the experiment and to events within the experimental set-up. The genetic view ascribes causes to an unrecognized effect of the experimenter's (...) sampling process prior to the experimental set-up. (shrink)

In this paper I contrast two causal explanations of the outcome of a set of laboratory experiments in population ecology conducted by Thomas Park in the 1940s and 1950s. These experiments shed light on the problem of adducing evidence for the operation of competition (see Lloyd 1987 for a recent philosophical discussion) and are central to the group selection controversy because they form the empirical base for experimental studies of group selection conducted over the last 12 years (see Griesemer (...) and Wade 1988 for an analysis).The experiments investigated competition in laboratory populations of two flour beetle species, Tribolium confusum and Tribolium castaneum, but the results were more complex and interesting than expected; in addition to demonstrating competition in a laboratory system, they exhibited what Park called “competitive indeterminacy.” This phenomenon was of great interest to statisticians, who devised stochastic models to describe its demographic basis (e.g., M.S. Bartlett, P.H. Leslie, and J. Neyman; see Mertz 1972 for references). (shrink)

In the literature on scientific explanation two types of pluralism are very common. The first concerns the distinction between explanations of singular facts and explanations of laws: there is a consensus that they have a different structure. The second concerns the distinction between causal explanations and uni.cation explanations: most people agree that both are useful and that their structure is different. In this article we argue for pluralism within the area of causal explanations: we claim that the (...) structure of a causal explanation depends on the causal structure of the relevant fragment of the world and on the interests of the explainer. (shrink)

I use a contrastive theory of causal explanation to analyze the notion of a genetic trait. The resulting definition is relational, an implication of which is that no trait is genetic always and everywhere. Rather, every trait may be either genetic or non-genetic, depending on explanatory context. I also outline some other advantages of connecting the debate to the wider causation literature, including how that yields us an account of the distinction between genetic traits and genetic dispositions.

We report two Experiments to compare counterfactual thoughts about how an outcome could have been different and causal explanations about why the outcome occurred. Experiment 1 showed that people generate counterfactual thoughts more often about controllable than uncontrollable events, whereas they generate causal explanations more often about unexpected than expected events. Counterfactual thoughts focus on specific factors, whereas causal explanations focus on both general and specific factors. Experiment 2 showed that in their spontaneous counterfactual thoughts, people focus (...) on normal events just as often as exceptional events, unlike in directed counterfactual thoughts. The findings are consistent with the suggestion that counterfactual thoughts tend to focus on how a specific unwanted outcome could have been prevented, whereas causal explanations tend to provide more general causal information that enables future understanding, prediction, and intervention in a wide range of situations. (shrink)

This book provides a post-positivist theory of deterministic and probabilistic causality that supports both quantitative and qualitative explanations. Features of particular interest include the ability to provide true explanations in contexts where our knowledge is incomplete, a systematic interpretation of causal modeling techniques in the social sciences, and a direct realist view of causal relations that is compatible with a liberal empiricism. The book should be of wide interest to both philosophers and scientists. Originally published in 1989. The (...) Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. (shrink)