Causal reasoning with causal graphs in educational technology research

Joshua Weidlich*, Ben Hicks, Hendrik Drachsler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today, a set of tools is available that can help researchers reason about cause-and-effect, irrespective of the particular research design or approach. This theoretical paper introduces such a tool, a simple graphical formalism that can be used to reason about potential sources of bias. We further explain how causal graphs differ from structural equation models and highlight the value of explicit causal inference. The final section shows how causal graphs can be used in several stages of the research process, whether researchers plan to conduct observational or experimental research.

Original languageEnglish
JournalEducational Technology Research and Development
Publication statusE-pub ahead of print - 30 May 2023


  • Causal graphs
  • Causal inference
  • Directed acyclic graphs
  • Experimental research
  • Observational research
  • Research to improve


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