MSBBSS Thesis Project
This repository serves as a research archive for the thesis project Discovering Cyclic Causal Models in Psychological Research.
It contains the data, R code, figures, manuscript, supplementary material, and other subsidiary files related to the project. Detailed guidelines on how to reproduce all analyses are described in separate README files (see below).
Abstract
Statistical network models have become popular tools for analyzing multivariate psychological data.
In empirical practice, network parameters are often interpreted as reflecting causal relationships – an approach that can be characterized as a form of causal discovery.
Recent research has shown that undirected network models are likely to perform poorly as causal discovery tools in the context of discovering acyclic causal structures, a task for which many alternative methods are available.
However, acyclic causal models are likely unsuitable for many psychological phenomena, such as psychopathologies, which are often characterized by cycles or feedback loop relationships between symptoms.
A number of cyclic causal discovery methods have been developed, largely in the computer science literature, but they are not as well studied or widely applied in empirical practice.
In this paper, we provide an accessible introduction to the basics of cyclic causal discovery for empirical researchers.
We examine three different cyclic causal discovery methods and investigate their performance in typical psychological research contexts by means of a simulation study. We also demonstrate the practical applicability of these methods using an empirical example and conclude the paper with a discussion of how the insights we gain from cyclic causal discovery relate to statistical network analysis.
├── Thesis_KP.Rproj
├── manuscript
│ ├── manuscript_KP.tex
│ ├── ...
│ └── kyuriapa.bst
├── simulation
│ ├── 01_simulation.R
│ ├── 02_simulation_results.R
│ ├── 03_sensitivity_analysis1.R
│ ├── 04_sensitivity_analysis2.R
│ ├── 05_sensitivity_analysis3.R
│ ├── 06_extra_analysis.R
│ └── output
├── empirical_example
│ ├── 01_empircal_analysis.R
│ ├── 02_stability_analysis.R
│ ├── data
│ └── output
├── utils
│ ├── CCD_fnc.R
│ ├── data_generating_fnc.R
│ ├── eval_metrics_fnc.R
│ ├── plot_fnc.R
│ ├── searchAM_KP_fnc.R
│ └── true_ancestral_fnc.R
├── supplementary_material
│ ├── Supplementary_material.html
│ ├── ...
│ └── Supplementary_material.qmd
└── figures
├── Fig1.pdf
├── ...
└── FigJ1.pdf
| Folder | Contents |
|---|---|
manuscript |
It contains all relevant files associated with the main manuscript. • manuscript_KP.pdf: the finalized manuscript. • manuscript_KP.tex: a plaintext file that contains the source code to generate the manuscript. • references.bib: a bibliography file that contains all the references used in the manuscript. • kyuriapa.bst: a BibTeX style file used to render APA formatting and style of references. |
simulation |
It contains all R scripts required to run the simulation studies. • 01_simulation.R: simulate models, generate data, and run the algorithms. • 02_simulation_results.R: evaluate performance of algorithms and create figures. • 03_sensitivity_analysis1.R: run the secondary simulation with randomly sampled coefficients for the regression matrix • 04_sensitivity_analysis2.R: conduct the secondary simulation by randomly sampling coefficients only with positive values for the regression matrix • 05_sensitivity_analysis3.R: run secondary analysis with varying • 06_extra_analysis.R: investigate the unexpected patterns in the 5-variable dense conditions from the main simulation study.• output: results from all simulations are stored as Rdata files due to the considerable processing time required to run them. |
empirical_example |
It contains all R scripts required to run the empirical analysis. • 01_empirical_analysis.R: execute the empirical analysis by applying the CCD, FCI and CCI algorithms to the empirical data from McNally et al. (2017). • 02_stability_analysis.R: perform the stability analysis on the empirical data (Appendix I of the paper). • data: raw data that is publicly available on the Psychological Medicine Journal webpage. • output: results from the sensitivity analysis saved as Rdata files. |
utils |
It includes all the supporting functions required to run the aforementioned R scripts for conducting analyses. |
supplementary_material |
It contains all files related to the supplementary material of the paper. The supplementary material can be accessed via this link. |
figures |
It contains all the figures that are presented in the paper. |
If interested in reproducing the results of:
- simulation study, please refer to
guide_simuation. - empirical anlaysis, please refer to
guide_empirical.
R session & Pacakge information
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os macOS Ventura 13.0
system aarch64, darwin20
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Amsterdam
date 2023-05-01
rstudio 2022.07.2+576 Spotted Wakerobin (desktop)
pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
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BiocGenerics * 0.42.0 2022-04-26 [1] Bioconductor
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broom 1.0.4 2023-03-11 [1] CRAN (R 4.2.0)
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callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
car 3.1-2 2023-03-30 [1] CRAN (R 4.2.3)
carData 3.0-5 2022-01-06 [1] CRAN (R 4.2.0)
CCI.KP * 0.1.0 2023-01-21 [1] Github (KyuriP/CCI_KP@984bf12)
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promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
ps 1.7.4 2023-04-02 [1] CRAN (R 4.2.0)
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RBGL 1.72.0 2022-04-26 [1] Bioconductor
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[1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
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All data used in this study are simulated, except for the data used in the empirical analysis, which is publicly available on the Psychological Medicine Journal webpage. There is no security or privacy concerns associated with the use of either the simulated or empirical data in this study.
This repository, which serves as an archive of the study is accessible to the public on GitHub under the license type of GNU General Public License v3.0.
The maintenance and public accessibility of the project repository are managed by Kyuri Park. If you have any inquiries or feedback about the project, please feel free to contact Kyuri Park.