Causal inference in misinformation and conspiracy research

Psychological research has provided important insights into the processing of misinformation and conspiracy theories. Traditionally, this research has focused on randomized laboratory experiments and observational (non-experimental) studies seeking to establish causality via third-variable adjustment. However, laboratory experiments will always be constrained by feasibility and ethical considerations, and observational studies can often lead to unjustified causal conclusions or confused analysis goals. We argue that research in this field could therefore benefit from clearer thinking about causality and an expanded
methodological toolset that includes natural experiments. Using both real and hypothetical examples, we offer an accessible introduction to the counterfactual framework of causality and highlight the potential of instrumental variable analysis, regression discontinuity design, difference-in-differences, and synthetic control for drawing causal inferences. We hope that such an approach to causality will contribute to greater integration amongst the various misinformation- and conspiracy- adjacent disciplines, thereby leading to more complete theories and better applied interventions.

Keywords: causal inference, conspiracy theory, fake news, methodological triangulation, misinformation