Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach
School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
2 Department of Geography, University College London, Gower Street, London, WC1E 6BT, United Kingdom
* e-mail: firstname.lastname@example.org
Accepted: 27 November 2015
Published online: 18 December 2015
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.
Key words: smartphone data / causality / human behavior / stress modeling
© The Author(s), 2015