Oisín Ryan

Oisín Ryan

Statistics, Data Science, and Causal Inference

Posts by Collection

code

Reproducibility Archives

I strive to make the code I use for analyses in all of my papers open and available through reproducibility archives, found through my publications page

publications

A continuous time approach to intensive longitudinal data: What, Why and How

Published in Continuous Time Modeling in the Behavioral and Related Sciences, 2018

Recommended citation: Ryan, O., Kuiper, R. M., & Hamaker, E. L. (2018). A Continuous Time Approach to Intensive Longitudinal Data: What, Why and How? In In K. v. Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous Time Modeling in the Behavioral and Related Sciences. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-77219-6_2

Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks

Published in BMC Medicine, 2020

Recommended citation: Groen, R. N., Ryan, O., Wigman, J. T., Riese, H., Penninx, B. W., Giltay, E. J., Wichers, M. & Hartman, C. A. (2020). Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks. BMC medicine, 18(1), 1-17. https://link.springer.com/article/10.1186/s12916-020-01738-z

(Pre-Print) Advancing the Network Theory of Mental Disorders: A Computational Model of Panic Disorder

Published in PsyArxiv, 2023

Pre-print here

Recommended citation: Robinaugh, D.J, Haslbeck, J. M. B., Waldorp, L., Kossakowski, J. J., Fried, E. I., Millner, A., McNally, R.J., Ryan, O., de Ron, J., van der Maas, H.L.J., van Nes, E.H., Scheffer, M., Kendler, K.S., & Borsboom, D. (2023, Feb 18). Advancing the Network Theory of Mental Disorders: A Computational Model of Panic Disorder https://psyarxiv.com/km37w/

talks

Equilibrium Causal Models: Connecting Causal Models & Dynamical Systems

Published:

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.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.

workshops

Introduction to Causal Modeling (Parts 1 and 2)

Published:

On November 7th and December 10th I gave a two part workshop to professional data scientists working at the University Medical Centre, Utrecht. The aim of the workshop was to introduce researchers to modern methods for causal modeling. Workshop materials for both days, including lecture slides and R lab materials, can be found here

Formal Theories in Psychology - What they are, why they matter, and how to build them

Published:

This hands-on workshop introduced formal theories (computational models) to a general psychology audience, covering the motivation for computational modeling approaches, examples, getting started with formalization using causal graphs, and practical coding examples. The material of the workshop was prepared jointly with Don Robinaugh and Jonas Haslbeck. All workshop materials can be found here. The full workshop abstract can be found here