Studying the effects of intervention programmes on household energy saving behaviours using graphical causal models. (November 2018)

• Record Type: Journal Article
• Authors: Bhushan, Nitin; Steg, Linda; Albers, Casper
• Is Part Of: Energy research & social science. Volume 45(2018)
• Page Start: 75
• Page End: 80
• Publication Date: 2018-11
• Subjects: Causality -- Intervention evaluation -- Graphical causal models -- Directed acyclic graphs -- Confounding -- Collider bias; Power resources -- Social aspects -- Periodicals; Energy consumption -- Social aspects -- Periodicals; 333.7905
• DOI: 10.1016/j.erss.2018.07.027
• Languages: English
• ISSNs: 2214-6296

Abstract

Abstract: Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.