A Program to Help Find Causal Models of Nonexperimental Data

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text