Studying the effects of intervention programmes on household energy saving behaviours using graphical causal models

Dr. Nitin Bhushan, Linda Steg, Casper Albers

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)75-80
Number of pages6
JournalEnergy Research & Social Science
Volume45
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Artifical Intelligence
  • AI
  • causality
  • graphica lcausal models
  • directed acyclic graphs
  • confounding
  • collider bias

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