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Causal Reasoning with Causal Models
2022We introduce and discuss the use of Bayesian networks for causal modeling. Despite their growing popularity and utility in this application, numerous objections to it have been raised. We address the claims that Chickering's arc reversal rule undermines a causal interpretation and that failures of Reichenbach's Common Cause Principle, or again failures
Korb, K B +3 more
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Sociological Methods & Research, 1998
Latent class analysis (LCA) is an extremely useful and flexible technique for the analysis of categorical data, measured at the nominal, ordinal, or interval level (the latter with fixed or estimated scores). It is, first, a general measurement model, a particular kind of latent structure model that can be used for the investigation of the reliability
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Latent class analysis (LCA) is an extremely useful and flexible technique for the analysis of categorical data, measured at the nominal, ordinal, or interval level (the latter with fixed or estimated scores). It is, first, a general measurement model, a particular kind of latent structure model that can be used for the investigation of the reliability
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2008
The Rubin Causal Model (RCM) is a formal mathematical framework for causal inference, first given that name by Holland (1986) for a series of previous articles developing the perspective (Rubin, 1974; 1975; 1976; 1977; 1978; 1979; 1980). There are two essential parts to the RCM, and a third optional one. The first part is the use of ‘potential outcomes’
Guido W. Imbens, Donald B. Rubin
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The Rubin Causal Model (RCM) is a formal mathematical framework for causal inference, first given that name by Holland (1986) for a series of previous articles developing the perspective (Rubin, 1974; 1975; 1976; 1977; 1978; 1979; 1980). There are two essential parts to the RCM, and a third optional one. The first part is the use of ‘potential outcomes’
Guido W. Imbens, Donald B. Rubin
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Synthesis: Causal Models, Causal Knowledge
Cardiopulmonary Physical Therapy Journal, 2018This article presents the 2018 Linda Crane Memorial Lecture Award on Synthesis: Causal Models, Causal Knowledge. The synthesis is that practice is based on causal knowledge and that we can encode causal knowledge with causal models. It proposes the use of causal models to provide a synthesis of what we know to develop causal knowledge from which to ...
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2012
This chapter compares two causal models used in qualitative and quantitative research: an additive-linear model and a set-theoretic model. The additive-linear causal model is common in the statistical culture, whereas the set-theoretic model is often used (implicitly) in the qualitative culture. After providing an overview of the two causal models, the
Gary Goertz, James Mahoney
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This chapter compares two causal models used in qualitative and quantitative research: an additive-linear model and a set-theoretic model. The additive-linear causal model is common in the statistical culture, whereas the set-theoretic model is often used (implicitly) in the qualitative culture. After providing an overview of the two causal models, the
Gary Goertz, James Mahoney
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2005
AbstractHuman beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. This book presents the question, in cognitive terms: how do people construct and reason with the causal models we use to represent our world?
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AbstractHuman beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. This book presents the question, in cognitive terms: how do people construct and reason with the causal models we use to represent our world?
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Causal Connections in Causal Modeling
Population (French Edition), 1987N. Bo., Guillaume Wunsch
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2010
Abstract ‘Causal modelling’ is a general term that applies to a wide variety of formal methods for representing, and facilitating inferences about, causal relationships. The end of the twentieth century saw an explosion of work on causal modelling, with contributions from such fields as statistics, computer science, and philosophy; as ...
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Abstract ‘Causal modelling’ is a general term that applies to a wide variety of formal methods for representing, and facilitating inferences about, causal relationships. The end of the twentieth century saw an explosion of work on causal modelling, with contributions from such fields as statistics, computer science, and philosophy; as ...
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