Conquering confounds and covariates: methods, library and guidance
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Updated
Mar 21, 2024 - Python
Conquering confounds and covariates: methods, library and guidance
Confound-isolating cross-validation approach to control for a confounding effect in a predictive model.
☯︎[ACMMM'22] Official PyTorch Implementation of Towards Unbiased Visual Emotion Recognition via Causal Intervention
SNP abundance correlates with network degree
Shiny-Tool for investigation of metabolite-covariate relationships
Accounting for hidden confounders in estimates of dose-response curves from observational data.
Stratified Analysis using R - Beginner Level
blopmatch: Matching Estimator based on a Bilevel Optimization Problem
Source code for the case study of "Constructing weights based on the disease risk score to address confounding in observational studies"
R code for the Shiny app that accompanies Westfall & Yarkoni (2016)
Detects sufficient and necessary conditions for pattern inversion conditional on log transform
R package to implement high-dimensional confounding adjustment using continuous spike and slab priors
Comparison of different methods for adjusting for confounding in a Cox regression using simulated data in stata
Simulation of unmeasured confounding
Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
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