Investigating Causality in Human Behavior from Smartphone Sensor Data: A Quasi-Experimental Approach

TL;DR

This paper presents a quasi-experimental framework to analyze causal relationships in human behavior using smartphone sensor data, addressing the limitations of correlation-based studies and enabling insights into health-related factors.

Contribution

The authors develop and evaluate a generic quasi-experimental approach for causality analysis in observational smartphone data, which is novel in this context.

Findings

Exercise and outdoor activities reduce stress levels.

Reduced working hours slightly decrease stress.

Social interactions' impact on stress was analyzed.

Abstract

Smartphones 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.