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The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source thes...
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The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source these causal sentences, and finally produce a text summary gathering all the information connected by means of this causal graph. This procedure has three main steps. The first one is focused in the extraction, filtering and selection of those causal sentences that could have relevant information for the system. The second one is focused on the composition of a suitable causal graph, removing redundant information and solving ambiguity problems. The third step is a procedure able to read the causal graph to compose a suitable answer to a proposed causal question by summarizing the information contained in it. (C) 2016 Elsevier B.V. All rights reserved.
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Abstract Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has bec...
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Abstract Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation‐based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning
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Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting and clustering have b...
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Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting and clustering have been proposed to analyze this type of data. Time series data have been also used to study the effect of interventions overtime. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide an in-depth insight. These metrics and datasets can serve as benchmark for research in the field.
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Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on ri...
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Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.
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Introduction. The need to establish causality covers a fairly wide range of different industries with different specifics and approaches. Therefore, it becomes necessary to apply various methods to solve the assigned tasks (in the...
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Introduction. The need to establish causality covers a fairly wide range of different industries with different specifics and approaches. Therefore, it becomes necessary to apply various methods to solve the assigned tasks (in the context of causality), which is accompanied by the choice of a wide range of tools, depending on the task at hand. Purpose. The purpose of this work is a brief overview and analysis of modern methods, algorithms and technologies for detecting causation and the range of tasks in which the use of the appropriate tools takes place. Methods. Starting from the gold standards of causal identification and to more accurate, but limited by the range of conditions, algorithms, the current state, advantages and disadvantages of the use of tools are described. Result. The analysis of the current state of existing methods, algorithms and technologies for establishing causality is carried out the prospects for further development and improvement of tools for causal detection are examined. Conclusions. At the moment there is a large list of known methods, algorithms and technologies, there is a number of problems in which there is a need for more accurate detection of causality. The paper shows that most of the tools for establishing causality give good results for acyclic structures, at the same time they can give false positive conclusions for cyclic structures. Well-known world scientific institutions and leading corporations of computer technology are fruitfully engaged in the development and implementation of more and more perfect tools for establishing causality in order to develop automated software projects close to human thinking.
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In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ?adequacy? remains underdeveloped in that p...
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In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ?adequacy? remains underdeveloped in that paper, in this study we offer a discussion of what it means for a definition of causality to be adequate to macroeconomics. One of the meanings of adequacy is that the definition of causality describes the types of relations for which macroeconomic causal models stand for. On this understanding of adequacy, we take issue with Henschen?s claim. We argue that his manipulationist definition is only applicable to a sample of causal models used by macroeconomists. There are other sets of macroeconomic causal models to which probabilistic and mechanistic definitions seem more adequate. We show relevant examples to support this claim and conclude that a moderate causal pluralism is an adequate stance with respect to macroeconomic causal models.
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In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ?adequacy? remains underdeveloped in that p...
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In his recent paper in the Journal of Economic Methodology, Tobias Henschen puts forth a manipulationist definition of macroeconomic causality that strives for adequacy. As the notion of ?adequacy? remains underdeveloped in that paper, in this study we offer a discussion of what it means for a definition of causality to be adequate to macroeconomics. One of the meanings of adequacy is that the definition of causality describes the types of relations for which macroeconomic causal models stand for. On this understanding of adequacy, we take issue with Henschen?s claim. We argue that his manipulationist definition is only applicable to a sample of causal models used by macroeconomists. There are other sets of macroeconomic causal models to which probabilistic and mechanistic definitions seem more adequate. We show relevant examples to support this claim and conclude that a moderate causal pluralism is an adequate stance with respect to macroeconomic causal models.
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? 2022 Elsevier Inc.Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are i...
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? 2022 Elsevier Inc.Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners’ concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners’ concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.
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Though causal order of message delivery sim- plifies the design and development of distributed applications, the overhead of enforcing it is not negligible. We claim that a causal order algorithm which does not send any redundant ...
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Though causal order of message delivery sim- plifies the design and development of distributed applications, the overhead of enforcing it is not negligible. We claim that a causal order algorithm which does not send any redundant infor- mation is efficient in the sense of communication overhead. We characterize and classify the redundant information into four cat- egories: information regarding just delivered, already delivered, just replaced, and already replaced messages. We propose an ef- ficient causal multicast algorithm which prevents propagation of these redundant information.
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This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from-or the same as-the tradi...
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This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from-or the same as-the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.
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