The Causal Interaction between Complex Subsystems

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.

Hylomorphism

The chapter provides a discussion of hylomorphism, a doctrine associated with Aristotle and his medieval followers according to which objects are compounds of matter and form. Two strands of contemporary hylomorphism are examined, one of which invokes a kind of downward causation. Another ‘modest’ strand regards forms as essences, the what it is to be (what it takes to be) something of a particular kind: a tree, a rabbit, an electron. This leaves open the nature of matter. Aristotle might or might not have embraced ‘prime matter’, but his account of change appears to call for a material something underlying changes among the elemental stuffs. The upshot is a seamless ‘blancmange’ universe apparently inhospitable to motion and to causal interaction among distinct objects.

The causal interaction in human basal ganglia

The experimental study of the human brain has important restrictions, particularly in the case of basal ganglia, subcortical centers whose activity can be recorded with fMRI methods but cannot be directly modified. Similar restrictions occur in other complex systems such as those studied by Earth system science. The present work studied the cause/effect relationships between human basal ganglia with recently introduced methods to study climate dynamics. Data showed an exhaustive (identifying basal ganglia interactions regardless of their linear, non-linear or complex nature) and selective (avoiding spurious relationships) view of basal ganglia activity, showing a fast functional reconfiguration of their main centers during the execution of voluntary motor tasks. The methodology used here offers a novel view of the human basal ganglia which expands the perspective provided by the classical basal ganglia model and may help to understand BG activity under normal and pathological conditions.

E-values for effect heterogeneity and conservative approximations for causal interaction

We provide sensitivity analyses for unmeasured confounding in estimates of effect heterogeneity and causal interaction.

Efficient Decomposition of Bayesian Networks With Non-graded Variables

Elicitation, estimation and exact inference in Bayesian Networks (BNs) are often difficult because the dimension of each Conditional Probability Table (CPT) grows exponentially with the increase in the number of parent variables. The Noisy-MAX decomposition has been proposed to break down a large CPT into several smaller CPTs exploiting the assumption of causal independence, i.e., absence of causal interaction among parent variables. In this way, the number of conditional probabilities to be elicited or estimated and the computational burden of the joint tree algorithm for exact inference are reduced. Unfortunately, the Noisy-MAX decomposition is suited to graded variables only, i.e., ordinal variables with the lowest state as reference, but real-world applications of BNs may also involve a number of non-graded variables, like the ones with reference state in the middle of the sample space (double-graded variables) and with two or more unordered non-reference states (multi-valued nominal variables). In this paper, we propose the causal independence decomposition, which includes the Noisy-MAX and two generalizations suited to double-graded and multi-valued nominal variables. While the general definition of BN implicitly assumes the presence of all the possible causal interactions, our proposal is based on causal independence, and causal interaction is a feature that can be added upon need. The impact of our proposal is investigated on a published BN for the diagnosis of acute cardiopulmonary diseases.

Population Aging and Health Expenditures in EU Member States

All the countries are faced with the population aging resulting from the rising life expectancy and decreasing fertility rates at the present time and in turn have experienced many social and economic implications. In this research, the authors explore the causal interaction between population aging and health expenditures in a sample of EU member economies during the 2000-2018 period through Dumitrescu and Hurlin causality analysis. The causality analysis revealed a unilateral causality from health expenditures to population aging.