Conditional learning through causal models

AbstractSummer break study designs are used in educational research to disentangle school from non-school contributions to social performance gaps. The summer breaks provide a natural experimental setting that allows for the measurement of learning progress when school is not in session, which can help to capture the unfolding of social disparities in learning that are the result of non-school influences. Seasonal comparative research has a longer tradition in the U.S. than in Europe, where it is only at its beginning. As such, summer setback studies in Europe lack a common methodological framework, impairing the possibility to draw lines across studies because they differ in their inherent focus on social inequality in learning progress. This paper calls for greater consideration of the parameterization of “unconditional” or “conditional” learning progress in European seasonal comparative research. Different approaches to the modelling of learning progress answer different research questions. Based on real data and constructed examples, this paper outlines in an intuitive fashion the different dynamics in inequality that may be simultaneously present in the survey data and distinctly revealed depending on whether one or the other modeling strategy of learning progress is chosen. An awareness of the parameterization of learning progress is crucial for an accurate interpretation of the findings and their international comparison.

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Cyclic quantum causal models

Nature Communications ◽

10.1038/s41467-020-20456-x ◽

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.

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Optimizing Consistency and Coverage in Configurational Causal Modeling

Sociological Methods & Research ◽

10.1177/0049124121995554 ◽

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.

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

BMC Public Health ◽

10.1186/s12889-021-10214-z ◽

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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