Causal Models
Abstracting Causal Models
... across causal models, rather than merely within a single causal ...abstracting causal models of large complexity into simpler causal models with only a few variables is of ...
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IMPACTS OF TRAFFIC INTERVENTIONS ON ROAD SAFETY: AN APPLICATION OF CAUSAL MODELS.
... a causal link between traffic interventions and road accidents remains unclear due to the complex character of traffic ...formal causal models makes it difficult fully to address issues such as ...
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Causal Models and the Logic of Counterfactuals
... Causal models provide a framework for making counterfactual predic- tions, making them useful for evaluating the truth conditions of coun- terfactual ...current causal models for ...
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Discounting and augmentation in causal conditional reasoning: causal models or shallow encoding?
... the causal model theory insofar as the pattern of main effects it predicts provided a better fit to the data than that predicted by mental models, although the main effect of the consequent was not ...
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Filtering Semantics for Counterfactuals: Bridging Causal Models and Premise Semantics
... concerning causal processes) may include the kind of information that is normally included in causal ...about causal dependencies and independencies between relevant events and (b) informa- tion ...
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Thermodynamics of Quantum Causal Models: An Inclusive, Hamiltonian Approach
... Furthermore, it cannot be overemphasized that the operational framework of quantum stochastic thermo- dynamics equips a large class of quantum causal models with a consistent thermodynamic interpretation, ...
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Learning Linear Cyclic Causal Models with Latent Variables
... Cyclic Causal Discovery algorithm (CCD, Richardson, 1996) that allows for cycles but not latent variables, and the Fast Causal Inference algorithm (FCI, Spirtes et ...
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Markov Properties for Linear Causal Models with Correlated Errors
... Linear causal models called structural equation models (SEMs) are widely used for causal reasoning in social sciences, economics, and artificial intelligence (Goldberger, 1972; Bollen, 1989; ...
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A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data
... ABSTRACT Causal models including genetic factors are important for understanding the presentation mechanisms of complex ...threshold models have been the primary approach to fitting genetic ...
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Discovering Quantum Causal Models
... causal models. Naeger (2015) and Evans (2015) both advance quantum causal models that take Pearl’s characterisation to be correct, but relax one of the key assumptions, allowing for ...
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Discovering cyclic causal models with latent variables: a general SAT-based procedure
... of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or experimental data ...of causal pathways, which permits the integra- tion of ...
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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
... a causal influence ...extract causal relations from text (these systems are not described in detail ...a causal influence between two Concepts (a subject and an object), each of which is linked to ...
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Causal models in epidemiology: past inheritance and genetic future
... In a rather simplified way, causation involves the relation- ship between at least two entities, an agent and a disease. Historically, at least two distinct eras of medical causality can be distinguished. The first era ...
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Search for Additive Nonlinear Time Series Causal Models
... of causal inference algorithms that are based on model scores, such as Bayesian posteriors, are unable to handle either latent variables or feedbacks, except under ex- tra constraints (Silva et ...linear ...
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Causal models for decision making via integrative inference
... interpret the regression matrix causally [Granger, 1969, L¨ utkepohl, 2006] (sometimes lags of length more than 1 are used as well). While this method may yield reasonable results in certain cases, it obviously can go ...
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A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data.
... We present a hybrid constraint based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) ...
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Algebraic discrete causal models
... as causal in some way. This has led to the develop- ment of the Causal Bayesian Network, using a non-parametric representation based on structural equation ...The causal Bayesian network is often ...
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A Clinical Decision Support System based on Ontology and Causal Reasoning Models
... graphical causal models able to illustrate the qualitative population assumptions, and the sources of bias, that are not easily noticed with other approaches (Greenland & Brumback, ...2002). ...
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Causal Control: A Rationale for Causal Selection
... characterize causal selection as involving three main ...the causal control they have over this ...make causal selection for disease much different, and arguably much easier, than causal ...
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The Relationship between Heuristic, Causal and Statistical Models of Knowledge, and Big Data
... statistical models may be a close one, with both the heuristic and statistical models homing in on the most common faults, as might be experienced by human experts and is ...
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