The Experts below are selected from a list of 20220 Experts worldwide ranked by ideXlab platform
J. A. Maxwell - One of the best experts on this subject based on the ideXlab platform.
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The Importance of Qualitative Research for Causal Explanation in Education
Qualitative Inquiry, 2012Co-Authors: J. A. MaxwellAbstract:The concept of causation has long been controversial in qualitative research, and many qualitative researchers have rejected Causal Explanation as incompatible with an interpretivist or constructiv...
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Causal Explanation, Qualitative Research, and Scientific Inquiry in Education
Educational Researcher, 2004Co-Authors: J. A. MaxwellAbstract:A National Research Council report, Scientific Research in Education, has elicited considerable criticism from the education research com- munity, but this criticism has not focused on a key assumption of the report—its Humean, regularity conception of Causality. It is ar- gued that this conception, which also underlies other arguments for “scientifically-based research,” is narrow and philosophically out- dated, and leads to a misrepresentation of the nature and value of qualitative research for Causal Explanation. An alternative, realist ap- proach to Causality is presented that supports the scientific legiti- macy of using qualitative research for Causal investigation, reframes the arguments for experimental methods in educational research, and can support a more productive collaboration between qualita- tive and quantitative researchers.
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Using Qualitative Methods for Causal Explanation
Field Methods, 2004Co-Authors: J. A. MaxwellAbstract:The view that qualitative research methods can be used to identify Causal relationships and develop Causal Explanations is now accepted by a significant number of both qualitative and quantitative researchers. However, this view is still controversial, and a comprehensive justification for this position has never been presented. This article presents such a justification, addressing both recent philosophical developments that support this position and the actual research strategies that qualitative researchers can use in Causal investigations.
S. Nishida - One of the best experts on this subject based on the ideXlab platform.
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Toward generating Causal Explanation: qualitative simulation with association mechanism to quantitative information
IEEE Transactions on Industrial Electronics, 1997Co-Authors: M. Akiyoshi, S. NishidaAbstract:One of the central issues of qualitative reasoning is generating Causal Explanation in response to a user's query. The construction of an adequate model is crucial to the generation of a valid Explanation in the case of large scale systems. This paper describes a new approach to the model construction based on using a Causal ordering algorithm and qualitative conversion of numerical data. Our approach involves two key ideas: (1) a qualitative model is constructed from a unique quantitative model and (2) simulated qualitative behavior aspects are compared with numerical data. This approach is illustrated by using it to generate Causal Explanation related to the operation of a nuclear power plant.
Jun Zhao - One of the best experts on this subject based on the ideXlab platform.
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CNCL - Towards Causal Explanation Detection with Pyramid Salient-Aware Network
2020Co-Authors: Xinyu Zuo, Yubo Chen, Kang Liu, Jun ZhaoAbstract:Causal Explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of Causal Explanation analysis, which determines whether a Causal Explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect Causal Explanations on messages. PSAN can assist in Causal Explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task.
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Towards Causal Explanation Detection with Pyramid Salient-Aware Network.
arXiv: Computation and Language, 2020Co-Authors: Xinyu Zuo, Yubo Chen, Kang Liu, Jun ZhaoAbstract:Causal Explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of Causal Explanation analysis, which determines whether a Causal Explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect Causal Explanations on messages. PSAN can assist in Causal Explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task.
James Woodward - One of the best experts on this subject based on the ideXlab platform.
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some varieties of non Causal Explanation
2018Co-Authors: James WoodwardAbstract:This chapter explores the possibility of weakening the criteria for Causal Explanation in Making Things Happen (MTH) to yield various forms of non-Causal Explanation. These include the following: (1) retaining the idea that Explanations must answer what if things had been different questions (the w-question requirement) but dropping the requirement the answers to such questions must take the form of claims about what would happen under interventions. (2) Retaining the w- question requirement but allowing generalizations that hold for mathematical or conceptual reasons to figure in Explanations. (3) Dropping the w-question requirement to accommodate the role of information about irrelevance in Explanation.
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Oxford Scholarship Online - Some Varieties of Non-Causal Explanation
Oxford Scholarship Online, 2018Co-Authors: James WoodwardAbstract:This chapter explores the possibility of weakening the criteria for Causal Explanation in Making Things Happen (MTH) to yield various forms of non-Causal Explanation. These include the following: (1) retaining the idea that Explanations must answer what if things had been different questions (the w-question requirement) but dropping the requirement the answers to such questions must take the form of claims about what would happen under interventions. (2) Retaining the w- question requirement but allowing generalizations that hold for mathematical or conceptual reasons to figure in Explanations. (3) Dropping the w-question requirement to accommodate the role of information about irrelevance in Explanation.
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counterfactuals and Causal Explanation
International Studies in The Philosophy of Science, 2004Co-Authors: James WoodwardAbstract:This article defends the use of interventionist counterfactuals to elucidate Causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like Causal relevance and Causal mechanism. Contrary to what both authors suppose, counterfactuals are not “unscientific”—a substantial tradition within statistics and the Causal modelling literature makes heavy use of them.
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making things happen a theory of Causal Explanation
2003Co-Authors: James WoodwardAbstract:Woodward's long awaited book is an attempt to construct a comprehensive account of causation Explanation that applies to a wide variety of Causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.
Sally Shrapnel - One of the best experts on this subject based on the ideXlab platform.
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quantum Causal Explanation or why birds fly south
European journal for philosophy of science, 2014Co-Authors: Sally ShrapnelAbstract: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.