Natural Experiments: Missed Opportunities for Causal Inference in Psychology

Michael P. Grosz*, Adam Ayaita, Ruben C. Arslan, Susanne Buecker, Tobias Ebert, Paul Hünermund, Sandrine R. Müller, Sven Rieger, Alexandra Zapko-Willmes, Julia M. Rohrer

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Knowledge about causal effects is essential for building useful theories and designing effective interventions. The preferred design for learning about causal effects is randomized experiments (i.e., studies in which the researchers randomly assign units to treatment and control conditions). However, randomized experiments are often unethical or unfeasible. On the other hand, observational studies are usually feasible but lack the random assignment that renders randomized experiments causally informative. Natural experiments can sometimes offer unique opportunities for dealing with this dilemma, allowing causal inference on the basis of events that are not controlled by researchers but that nevertheless establish random or as-if random assignment to treatment and control conditions. Yet psychological researchers have rarely exploited natural experiments. To remedy this shortage, we describe three main types of studies exploiting natural experiments (standard natural experiments, instrumental-variable designs, and regression-discontinuity designs) and provide examples from psychology and economics to illustrate how natural experiments can be harnessed. Natural experiments are challenging to find, provide information about only specific causal effects, and involve assumptions that are difficult to validate empirically. Nevertheless, we argue that natural experiments provide valuable causal-inference opportunities that have not yet been sufficiently exploited by psychologists.
Original languageEnglish
JournalAdvances in Methods and Practices in Psychological Science
Issue number1
Number of pages15
Publication statusPublished - 2024


  • Causality
  • Nonexperimental
  • Regression discontinuity design
  • Instrumental variable estimation
  • Observational studies

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