Things you didn’t know you needed: Graphical Causal Models


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Elise Kalokerinos

Dr Julia Rohrer
University of Leipzig

Correlation does not imply causation -- but correlations are often all we can get. In this session, I will provide a brief non-technical introduction to graphical causal models. These models are a powerful tool for researchers working with observational data, but also for experimentalists: A broad range of common inference problems (e.g., mediation analysis, missing data, generalization across populations and settings) can be greatly clarified through a causal lens. Psychologists are fond of lists of threats to validity, but maybe it is time to move on to a more systematic and transparent approach in which we explicitly spell out licensing assumptions.

About the speaker

Julia Rohrer finished her doctoral degree as a fellow of the International Max Planck Research School on the Life Course in 2019. A personality psychologist by training, her work covers a broad range of topics, including the effects of birth order, age patterns in personality, and the determinants and correlates of subjective well-being. Her methodological interests include causal inference on the basis of observational data, data analytic flexibility, and research transparency.