Confound-isolating cross-validation approach to control for a confounding effect in a predictive model.
Conquering confounds and covariates: methods, library and guidance
NeurIPS 2024 (spotlight): A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis
☯︎[ACMMM'22] Official PyTorch Implementation of Towards Unbiased Visual Emotion Recognition via Causal Intervention
A library for minimizing the effects of confounding covariates
Shiny-Tool for investigation of metabolite-covariate relationships
SNP abundance correlates with network degree
DAG Builder
Accounting for hidden confounders in estimates of dose-response curves from observational data.
Code for a probabilistic sensitivity analysis of an unmeasured confounder
An R-Package to perform Time-Dependent Matching for Causal Inference in Discrete and Continuous Time
Source code for off-policy evaluation and optimization in the presence of unobserved confounding, accepted to AISTATS 2024.
Stratified Analysis using R - Beginner Level
blopmatch: Matching Estimator based on a Bilevel Optimization Problem
When Shift Happens - Confounding is to Blame (ICLR 2026)
Confounding issues in air pollution epidemiology: an empirical assessment with the UK Biobank cohort
Coursework, Stata code, and notes for PBHS 31001: Epidemiologic Methods (Winter 2024, University of Chicago). Topics include bias, confounding, effect modification, cohort and case-control study design, and logistic/Poisson regression. The course emphasizes observational study design and practical applications using Stata.
R package to implement high-dimensional confounding adjustment using continuous spike and slab priors
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