Correlation and causation between the UN Human Development Index and national and personal wealth and resource exploitation

Human development is increasingly on global policy agendas, in particular related to the Sustainable Development Goals. Here, the UN Human Development Index is analysed for correlation and causation with economic and resource parameters using novel quantitative techniques. Global datasets at national resolution are used to explore correlation and causation with the HDI. The whole HDI is not correlated to national totals of wealth or resource use, but is strongly correlated to personal wealth and resource use. The multi-spatial convergence cross mapping method is adapted to shed light on causation in this system. It is shown that the HDI is tightly linked to the economy and to personal resource use. Analysis of the HDI sub-indices reveals subtleties easily overlooked. For example, it is shown that access to water and electricity strongly influence GNI. It is shown that simple resource accumulation/exploitation is less important in determining HDI growth than personal wealth and access to resources. That is, equitable distribution is more effective than gross accumulation in influencing the HDI. Strong feedback means that investments in water treatment and distribution networks, for example, will have strong effects on HDI change, a conclusion that may play an important role in national developmental policy debate

Understanding causation via correlations and linear response theory

In spite of the (correct) common-wisdom statement correlation does not imply causation, a proper employ of time correlations and of fluctuation-response theory allows to understand the causal relations between the variables of a multi-dimensional linear Markov process. It is shown that the fluctuation-response formalism can be used both to find the direct causal links between the variables of a system and to introduce a degree of causation, cumulative in time, whose physical interpretation is straightforward. Although for generic non-linear dynamics there is no simple exact relationship between correlations and response functions, the described protocol can still give a useful proxy also in presence of weak nonlinear terms

Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach

Smartphones and wearables have become an indispensable part of our daily life. Their improved sensing and computing capabilities bring new opportunities for human behavior monitoring and analysis. Most work so far has been focused on detecting correlation rather than causation among features extracted from smartphone data. However, pure correlation analysis does not offer sufficient understanding of human behavior. Moreover, causation analysis could allow scientists to identify factors that have a causal effect on health and well-being issues, such as obesity, stress, depression and so on and suggest actions to deal with them. Finally, detecting causal relationships in this kind of observational data is challenging since, in general, subjects cannot be randomly exposed to an event. In this article, we discuss the design, implementation and evaluation of a generic quasi-experimental framework for conducting causation studies on human behavior from smartphone data. We demonstrate the effectiveness of our approach by investigating the causal impact of several factors such as exercise, social interactions and work on stress level. Our results indicate that exercising and spending time outside home and working environment have a positive effect on participants stress level while reduced working hours only slightly impact stress