The scope of the present thesis is four-fold. First, to clarify and explain the means-ends framework and step-by-step analysis of the Austrian school. Second, to apply this framework to the Austrian theory of exchange rates. Third, to link the framework with most of the existing Austrian research related with the exchange rate theory and discuss this research. And fourth, to confront the Austrian economics with two mainstream approaches - Dornbusch?s overshooting model and short-run portfolio balance model. Message springing from this confrontation is twofold. First, the fundamental differences between present-day mainstream methods are envisaged. And second, the fact of possibility of mutual enrichment of both approaches from each other despite of completely different methodological backgrounds is suggested.

<p>Finding cause and effect relationships is one of the quintessential questions throughout many of the empirical sciences, AI, and Machine Learning. This dissertation develops graphical conditions and efficient algorithms for two problems, linear identification and imitation learning. For the first problem, it is well-known that correlation does not imply causation, so linear regression doesn’t necessarily find causal relations even in the limit of a large sample size. Over the past century, a plethora of methods has been developed for identifying interventional distributions given a combination of assumptions about the underlying mechanisms (e.g., linear functional dependence, causal diagram) and observational data. We characterize the computational complexity of several existing graphical criteria and develop new polynomial-time algorithms that subsume existing disparate efficient approaches. The proposed methods constitute the current state of the art in terms of polynomial-time identification coverage. In words, our methods have the capability of identifying the maximal set of structural coefficients when compared to any other efficient algorithms found in the literature.</p> <p>The second problem studied in the dissertation is Causal Sequential Imitation Learning, which is concerned with an agent that aims to learn a policy by observing an expert acting in the environment, and mimicking this expert's observed behavior. Sometimes, the agent (imitator) does not have access to the same set of observations or sensors as the expert, which gives rise to challenges in correctly interpreting expert actions. We develop necessary and sufficient conditions for the imitator to obtain identical performance to the expert in sequential settings given the domain’s causal diagram, and create a polynomial-time algorithm for finding the covariates to include when generating an imitating policy.</p> <p><br></p>

Thesis advisor: Eileen Sweeney / Motion is the central phenomenon that Aristotle's physics endeavors to explain, and the whole superstructure of his natural philosophy is bound to it. This was certainly understood by Thomas Aquinas, who produced a most careful and thorough account of the Aristotelian theory of motion. What is rarely recognized is that in so doing he developed and improved that theory in a number of respects. This dissertation is a study of the theory of physical motion in Aquinas. It has two principal results. The first regards the concept of motion itself. Aquinas accepts Aristotle's definition of motion, but gives his own explanation of it, one which employs non-Aristotelian ideas like participation, and places motion in a more general and cohesive system of relations between non-being and being. The second regards what is called natural motion, i.e., that which inanimate matter exhibits of its own accord. How Aquinas explains this has rarely been understood and has frequently been misunderstood. I provide a thorough, systematic treatment that sets forth the principles underlying Aquinas's theory of natural motion, explains in what sense such motion is said to be caused, and notes – what commentators are prone to overlook – the critical role played by analogy as an aid to grasp the causality of natural motion. Chapter 1 briefly introduces Aquinas as a medieval "physicist", i.e., natural philosopher, specifies the scope of the dissertation, and lays out the path to be followed in the succeeding chapters. Chapter 2 summarizes what Aristotle says about motion in his Physics. His definition of motion is presented, and the terms in which it is given are analyzed. As the definition is famously perplexing, and its meaning a matter of debate, the opinions of a number of modern commentators are reviewed. After having explained the primacy that Aristotle assigns to locomotion and the connection he establishes between motion and time, the chapter concludes with a section on the causes of motion. This covers Aristotle's arguments for the eternity of motion, his denial of pure self-motion, introduces his views on animal, natural, and violent motion, and concludes with the first mover and first motion reached at the end of the Physics. Chapter 3 is a short introduction to the tradition of Aristotelian commentary. It contains brief reviews of some of what Simplicius, John Philoponus, Averroes, and Avicenna say about the Aristotelian theory of motion, with especial attention given to those aspects of it with which they disagree or which they find troublesome. The next three chapters are devoted to Aquinas. Chapter 4 covers how Aquinas interprets Aristotle's definition of motion and how he integrates it into his own metaphysics. The chapter begins with two preparatory notes. First, it introduces Aquinas as a commentator, and stresses the importance of having the context of the commentary tradition in mind when reading Aquinas, because he belongs to it and approached Aristotle in much the same way as earlier commentators. This has the consequence that his own developments are somewhat concealed, since what he says in his commentary on the Physics is presented as straightforward exposition, without any suggestion of originality. Second, modern readers are cautioned not to import modern senses of "motion" unwittingly into Aquinas, as this leads to confusion. The point is made that his Latin usage is regular and well-defined. After this, Aquinas's interpretation of the definition of motion is presented and what he means by calling it "imperfect act" is explained. This involves a sophisticated theory of the order of act to act, and incorporates the Neoplatonic language of participation and perfection. The next section of the chapter presents some of the analyses that Aquinas makes of motion, including its mental character and categorization. It is noteworthy, yet in fact quite unnoticed, that Aquinas provides a subtle and inventive solution to a modern debate over the proper interpretation of the definition of motion (the so-called "process" vs. "non-process" debate) which reconciles the two sides. Finally, I present a visual analogy as an aid for grasping how motion fits into a larger Thomistic metaphysical scheme concerning the relationship between non-being and being. Chapter 5 treats Aquinas's account of natural motion. It begins by noting some of the problems involved with Aristotle's explanation of natural motion, including, critically, that of the cause of such motion. It then highlights the work of James Weisheipl, who rejected a motor coniunctus interpretation of natural motion and offered in its stead his own, one which has since become well-known. But a careful examination of what Aquinas says shows that Weisheipl's interpretation of him is incorrect, and must also be rejected. The chapter then lays out (1) the principles of motion and their schematic organization; (2) Aquinas's theory of efficient causality and how we are to understand his denial of the possibility of action at a distance; (3) his use of analogies to indicate how we are to understand the efficient cause of natural motion; and then finally (4) shows, first, how the multiplicity and variation of the analogies lead commentators to misread Aquinas, and, then, how they illuminate other aspects of his theory of motion. Chapter 6 treats, in turn, the much debated principle that "everything that is moved is moved by another", and then the arguments that Aquinas gives for the existence of an unmoved mover, which he takes to be God. With regard to the first, it is pointed out that the theory of inertia is not nearly so fatal to the principle as many historians of science assume. Indeed, understood in light of what was said about Aquinas's understanding of the efficient causality involved in natural motion in the preceding chapter, it is compatible with inertia. However, a review of the three arguments Aquinas takes from Aristotle purporting to prove the principle are all found to be subject to serious objections. Greatest attention is given to the argument drawn from the claim that a thing cannot simultaneously be in act and in potency in the same respect. With regard to the second, it is shown that what Aquinas has to say about the unmoved mover is perplexed and inconsistent. Aquinas accepts two series of arguments from Aristotle, one from the Physics that concludes to a first moving cause, another from the Metaphysics that concludes to a final cause. From the former, Aquinas constructs his own argument from motion, most famously presented as the prima via, but at times he attempts to combine the two Aristotelian series. It is shown that these attempts at harmonization involve Aquinas in inconsistencies. What the prima via revision concludes to is also discussed. An important observation is that the prima via is manifestly intended by Aquinas to be an argument from physical motion. Yet numerous commentators, perceiving its weakness, attempt to recast it in a non-physical form, such that "motion" no longer signifies what Aquinas intends. The chapter ends with some cautious remarks on the state of these highly controversial topics. Chapter 7 concludes the dissertation, first, with a brief recapitulation of some of its major points, and then with some speculation as to what use its results may have for future scholarship. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Philosophy.

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No / Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics to infer causal relationships. The social and health sciences analyse population-level data using statistical methods to infer average causal relations. In diagnosis of disease, probabilistic statements are based on population-level causal knowledge combined with knowledge of a particular person¿s symptoms. For the physical sciences, the Salmon-Dowe account develops an analysis of causation based on the notion of process and interaction. In artificial intelligence, the development of graphical methods has leant impetus to a probabilistic analysis of causality. The biological sciences use probabilistic methods to look for evolutionary causes of the state of a current species and to look for genetic causal factors. This variegated situation raises at least two fundamental philosophical issues: about the relation between causality and probability, and about the interpretation of probability in causal analysis. In this book we bring philosophers and scientists together to discuss the relation between causality and probability, and the applications of these concepts within the sciences.

With the advent of online publishing of scientific research came an avalanche of electronic resources and repositories containing knowledge encoded in some form or another. In the domain of biomedical sciences, research is now being published at a faster-than-ever pace, with several thousand articles per day. It is impossible for any human being to process that amount of information in due time, let alone apply it to their own needs. Thus appeared the necessity of being able to automatically retrieve relevant documents and extract useful information from text. Although it is now possible to distil essential factual knowledge from text, it is difficult to interpret the connections between the extracted facts. These connections, also known as discourse relations, make the text coherent and cohesive, and their automatic discovery can lead to a better understanding of the conveyed knowledge. One fundamental discourse relation is causality, as it is the one which explains reasons and allows for inferences to be made. This thesis is the first comprehensive study which focusses on recognising discourse causality in biomedical scientific literature. We first construct a manually annotated corpus of discourse causality and analyse its characteristics. Then, a methodology for automatically recognising causal relations using text mining and natural language processing techniques is presented. Furthermore, we investigate the automatic identification of additional information about the polarity, certainty, knowledge type and source of causal relations. The entire methodology is evaluated by empirical experiments, whose results show that it is possible to successfully extract causal relations from biomedical literature. Finally, we provide an example of a direct application of our research and offer ideas for further research directions and possible improvements to our methodology.

The present thesis presents a causal approach to transitivity and proposes a model of transitivity based on the view that a single event is a single ‘causal impact’, which consists of a single causation and a single effect. It defines semantic intransitivity as events where the effect is borne by and expressed through the actor and semantic transitivity as events where the effect is borne by and expressed through the patient. It finds evidence for this definition in the phenomenon of ‘selective specification’ of action or result by verbs with actor and patient. Furthermore, it proposes that the verb eat has dual event structures, intransitive and transitive, and uses a Web data test to test and confirm this hypothesis.

This paper studies the price fluctuation from 2010 to 2016 of two major assets in China: real estate and stock. Equity price is found to Granger cause stock price while the reverse relationship is significant but less strong. The paper then studies whether the nature of the correlation depends on the type of city under consideration. This is achieved by grouping 25 cities into four city tiers based on their level of economic developments and conducting a linear causality test on each city tier. Housing price in first tier cities is found to be much more significantly correlated with stock price. Larger and more developed cities tend to have a stronger correlation with stock than smaller and less developed ones. In addition, the paper also studies the impact of the Chinese government’s recent home purchase restriction on the relationship between the two asset classes. However, the results are contradictory and are not consistent with expectation. The lack of significant results could be contributed to the inherent limitation of our data, as well as the complicated and sometimes confusing policy announcement mechanisms in China.

<p>The dependence on oil has increased in many nations as a result of increasing industrialization and oil has been the factor of many crises as well as many wars. This paper examines how the price of oil affects the unemployment in Sweden. The case of Sweden is interesting since its politics are very different compared to other industrialized countries when it comes to unemployment and benefits. Our main objective is to see whether a change in the oil price will cause a change in unemployment at a later stage. We perform linear regression analysis relating current changes in the variables and Granger causality tests to conclude if there exists a direct relationship.</p><p>The result we received from our linear regression test on current changes and our Granger causality test showed a relationship between the price of oil and unemployment in Sweden. In the linear regression relating current changes in these variables, a positive relationship was indicated. Due to the fact that some of the coefficient estimates are positive and some are negative in the Granger causality regressions, we can not conclude whether an increase in the price of oil will cause a positive or negative effect on unemployment.</p>