## What Paradox? Causal models as a remedy for statistical confusion

Talk, Young Statisticians Statistics Cafe, Utrecht, NL

Talk, Young Statisticians Statistics Cafe, Utrecht, NL

Talk, Methodology & Statistics Colloquium, Utrecht, NL

Talk, Workshop From Data to Causes: Perspectives on Causation from Psychology, Physics, and Dynamical Systems., Berlin

Feedback loops and cyclic relationships are a mainstay of dynamical systems modelling, yet they are conspicuously absent in much of causal inference, where Directed Acyclic Graphs (DAGs) abound. Cycles do indeed have a place in causal inference, yet, in order to understand them, it is necessary to map causal models and causal intuitions back onto dynamical systems. To do this we can make use of Equilibrium Causal Models (ECMs), causal abstractions of dynamical systems which a) allow researchers to reason about the long time-scale effects of interventions and b) can potentially be learned from cross-sectional data. In this short talk, we give a brief motivation of ECMs, discuss how they relate to contemporary issues in psychological research, and sketch a simulation study to assess the feasibility of learning cyclic causal structures in practice.

Talk, 9th European Congress of Methodology (EAM), Valencia, Spain

Talk, 9th European Congress of Methodology (EAM), Valencia, Spain

Talk, McNally Lab, Harvard Psychology Department (Invited talk), Cambridge, MA, USA"

Talk, Conference on Complex Systems (CCS), Thessaloniki, Greece

Talk, 30th Association for Psychological Science (APS) Annual Convention, San Francisco, USA

Talk, 9th Lab Meeting Dynamical Networks and Time Series Models (DynaNet), Amsterdam, NL

Talk, International Meeting of the Psychometric Society (IMPS), Zurich, Switzerland

Talk, 5th conference of the Society for Ambulatory Assessment (SAA 2017), Esch-sur-Alzette, Luxembourg

Talk, International Convention of Psychological Science (ICPS), Vienna, Austria