model-selection-sim-study

Simulation study of various variable selection techniques. Intended to examine the bias in parameter estimation in the context of causal health. In particular, we grapple with confounding, unseen confounding and bias amplification.

https://github.com/raspberryemma/model-selection-sim-study

Science Score: 44.0%

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Repository

Simulation study of various variable selection techniques. Intended to examine the bias in parameter estimation in the context of causal health. In particular, we grapple with confounding, unseen confounding and bias amplification.

Basic Info
  • Host: GitHub
  • Owner: RaspberryEmma
  • License: gpl-3.0
  • Language: HTML
  • Default Branch: main
  • Size: 14.7 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Model-Selection-Sim-Study

Simulation study of various variable selection techniques. Intended to examine the bias in parameter estimation in the context of causal health. In particular, we grapple with confounding, bias amplification, missingness and other features important to causal inference.

The folders "Jupyter", "data" and "plots" contain pre-computed results.

The simulation can currently be run in one of two ways: - In R / RStudio using the script "RunSim.R" - In Jupyter Notebook via the R Kernel using the notebook "RunSim.ipynb"

Still in development!

Owner

  • Name: Emma
  • Login: RaspberryEmma
  • Kind: user

PhD Student at University of Bristol studying Computational Statistics and Data Science (she / her)

Citation (CITATION.cff)

cff-version: 1.2.0

message: "If you use this software, please cite it as below."

authors:
  - family-names: Tarmey
    given-names:  Emma
    orcid: https://orcid.org/0009-0000-7214-4976

title: "Variable Selection Simulation Study"
version: 1.0.0
date-released: 2023-05-01

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