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

NeurIPS 2024 (spotlight): A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis

A library for minimizing the effects of confounding covariates

Shiny-Tool for investigation of metabolite-covariate relationships

SNP abundance correlates with network degree

Accounting for hidden confounders in estimates of dose-response curves from observational data.

Code for a probabilistic sensitivity analysis of an unmeasured confounder

blopmatch: Matching Estimator based on a Bilevel Optimization Problem

Detects sufficient and necessary conditions for pattern inversion conditional on log transform

code for paper on automating confounder control methods

R code for the Shiny app that accompanies Westfall & Yarkoni (2016)

An R package for working with confounders

Source code for the case study of "Constructing weights based on the disease risk score to address confounding in observational studies"

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

Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies

Stratified Analysis using R - Beginner Level