Non Confounding Covariates

Non-confounding covariates represent variables influencing either the treatment or outcome, but not both, in causal inference studies. Current research focuses on mitigating bias stemming from the improper handling of these covariates, particularly in high-dimensional data, using methods that disentangle their effects from those of true confounders. This involves developing algorithms and models, such as those based on mixture models or balancing scores, to accurately estimate causal effects even with incomplete knowledge of all confounders. Improved handling of non-confounding covariates is crucial for enhancing the reliability and validity of causal inferences across diverse fields, from healthcare to environmental science.

Papers

September 18, 2024

Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models
Morris de Haan, Philipp Hager
Mixed Effect Unbiased Learning Expert Annotation Large Scale Real World Non Confounding Covariates

November 9, 2023

Causal Inference from Text: Unveiling Interactions between Variables
Yuxiang Zhou, Yulan He
Text Modality Causal Inference Causal Effect Latent Feature Relevant Covariates Variable Model Biased Causal Effect Non Confounding Covariates Deep Interaction

October 21, 2023

Counterfactual Prediction Under Selective Confounding
Sohaib Kiani, Jared Barton, Jon Sushinsky, Lynda Heimbach, Bo Luo
Causal Inference Counterfactual Prediction Text Based Confounders Interpretable Causal Non Confounding Covariates

August 22, 2023

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?
Yonghe Zhao, Qiang Huang, Shuai Fu, Huiyan Sun
Causal Inference Text Based Confounders Confounding Bias Non Confounding Covariates

April 23, 2023

Meaningful Causal Aggregation and Paradoxical Confounding
Yuchen Zhu, Kailash Budhathoki, Jonas Kuebler, Dominik Janzing
Causal Pattern Causal Relation Cause Effect Causal System Non Confounding Covariates Q Aggregation

October 17, 2022

Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Sami Hamdan, Bradley C. Love, Georg G. von Polier, Susanne Weis, Holger Schwender, Simon B. Eickhoff, Kaustubh R. Patil
Machine Learning Confounding Bias Deep Leakage Non Confounding Covariates

September 25, 2022

Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies
Mauricio Tec, James Scott, Corwin Zigler
Representation Learning Causal Inference Causal Effect Earth System Text Based Confounders Spatial Interaction Non Confounding Covariates

December 22, 2021

Causal Inference Despite Limited Global Confounding via Mixture Models
Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman
Causal Inference Causal Relationship Bayesian Network Mixture Model Causal Relation Non Confounding Covariates