Conquering confounds and covariates in machine learning
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Updated
Feb 9, 2023 - Python
Conquering confounds and covariates in machine learning
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
R code for the Shiny app that accompanies Westfall & Yarkoni (2016)
Detects sufficient and necessary conditions for pattern inversion conditional on log transform
Source code for the case study of "Constructing weights based on the disease risk score to address confounding in observational studies"
Simulation of unmeasured confounding
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
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