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

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"

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

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

Stratified Analysis using R - Beginner Level