Causal inference in misinformation and conspiracy research

Psychological research has provided important insights into the processing of misinfor-
mation and conspiracy theories. Traditionally, this research has focused on randomized la-
boratory experiments and observational (non-experimental) studies seeking to establish
causality via third-variable adjustment. However, laboratory experiments will always be con-
strained 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 meth-
odological toolset that includes natural experiments. Using both real and hypothetical ex-
amples, we offer an accessible introduction to the counterfactual framework of causality
and highlight the potential of instrumental variable analysis, regression discontinuity de-
sign, 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 vari-
ous misinformation- and conspiracy- adjacent disciplines, thereby leading to more com-
plete theories and better applied interventions.