Simulating Generic Situations on Causal Models

1989 ◽  
pp. 177-186 ◽  
Author(s):  
Luca Console ◽  
Gianpaolo Molino ◽  
Roberta Pavia ◽  
Marco Signorelli ◽  
Pietro Torasso
Keyword(s):  
Causal Models

A Program to Help Find Causal Models of Nonexperimental Data

1989 ◽  
Vol 34 (12) ◽  
pp. 1086-1087
Author(s):  
Charles F. Chubb
Keyword(s):  
Causal Models

The Role of Causal Models in Analogical Inference

2010 ◽  
Vol 42 (8) ◽  
pp. 834-844
Author(s):  
Ting-Ting WANG ◽  
Lei MO
Keyword(s):  
Causal Models ◽  
Analogical Inference

Independence Relations Produced by Parameter Values in Causal Models

1990 ◽  
Vol 18 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Clark Glymour ◽  
Peter Spirtes ◽  
Richard Scheines ◽  
Keyword(s):  
Causal Models ◽  
Parameter Values ◽  
Independence Relations

scholarly journals Testing Graphical Causal Models Using the R Package “dagitty”

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ankur Ankan ◽  
Inge M. N. Wortel ◽  
Johannes Textor
Keyword(s):  
R Package ◽  
Causal Models

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals Optimizing Consistency and Coverage in Configurational Causal Modeling

2021 ◽  
pp. 004912412199555
Author(s):  
Michael Baumgartner ◽  
Mathias Ambühl
Keyword(s):  
Model Selection ◽  
Causal Inference ◽  
Comparative Methods ◽  
Causal Models ◽  
Causal Modeling ◽  
Model Fit

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

scholarly journals Conceptual causal models of socioeconomic status, family structure, family functioning and their role in public health

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Frederik Booysen ◽  
Ferdi Botha ◽  
Edwin Wouters
Keyword(s):  
Public Health ◽  
Socioeconomic Status ◽  
Family Functioning ◽  
Family Structure ◽  
Empirical Studies ◽  
Social Determinant ◽  
Causal Models ◽  
Family Socioeconomic Status ◽  
The Family ◽  
Social Determinant Of Health

AbstractSocial determinants of health frameworks are standard tools in public health. These frameworks for the most part omit a crucial factor: the family. Socioeconomic status moreover is a prominent social determinant of health. Insofar as family functioning is poorer in poor families and family structure and functioning are linked to health, it is critical to consider the pathways between these four constructs. In this correspondence, we reflect on how empirical studies of this conceptual nexus mirror two causal models. We conclude by reflecting on future directions for research in this field.

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