Unobserved Confounders

Unobserved confounders represent a significant challenge in causal inference, where the goal is to accurately estimate the causal effect of an intervention while accounting for variables influencing both the intervention and the outcome. Current research focuses on developing methods to mitigate the bias introduced by these unobserved variables, employing techniques such as optimal transport, variational autoencoders, instrumental variables, and causal learning frameworks within various model architectures (e.g., deep autoregressive models, neural controlled differential equations). Addressing unobserved confounders is crucial for reliable causal inference across diverse fields, improving the accuracy of predictions in areas like climate modeling, healthcare, and recommendation systems, and enabling more robust and fair decision-making.

Papers

August 2, 2023

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference
Yonghe Zhao, Qiang Huang, Siwei Wu, Yun Peng, Huiyan Sun
Causal Inference Variational Inference Unobserved Confounders Counterfactual Inference Variational Learning

June 22, 2023

Approximate Causal Effect Identification under Weak Confounding
Ziwei Jiang, Lai Wei, Murat Kocaoglu
Causal Effect Causal Effect Estimation Unobserved Confounders Causal Query Confounding Bias

June 1, 2023

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
Offline Reinforcement Learning Unobserved Confounders Unobserved Variable Latent Confounding

April 24, 2023

Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
Causal Inference Variational Autoencoder Causal Effect Estimation Unobserved Confounders Causal Strength Front Door Criterion

March 20, 2023

A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
Siyu Chen, Yitan Wang, Zhaoran Wang, Zhuoran Yang
Optimal Policy Policy Learning Unobserved Confounders Partial Observation Confounding Bias Offline Contextual Bandit

March 8, 2023

Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies
Jose M. Peña
Complementary Benefit Unobserved Confounders Relative Probability Proxy Dataset Proxy Metric Tighter Bound Unmeasured Confounding Model Sensitivity

March 4, 2023

Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Defu Cao, James Enouen, Yujing Wang, Xiangchen Song, Chuizheng Meng, Hao Niu, Yan Liu
Treatment Effect Causal Analysis Unobserved Confounders Irregular Time Series

February 26, 2023

Kernel Conditional Moment Constraints for Confounding Robust Inference
Kei Ishikawa, Niao He
Unobserved Confounders Conservative Value Estimation Robust Inference Offline Contextual Bandit Marginal Sensitivity Model

February 10, 2023

Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo
Unobserved Confounders Latent Confounders Debiased Recommendation Counterfactual Prompting Proximal Causal

February 1, 2023

Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
David Bruns-Smith, Angela Zhou
Offline Reinforcement Learning Unobserved Confounders Q Function Fitted Q Iteration Q Evaluation Robust Policy Evaluation

January 30, 2023

Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Technical Challenge Causal Inference Emerging Opportunity Unobserved Confounders Latent Confounding Observational Study

December 6, 2022

Causal Inference via Style Transfer for Out-of-distribution Generalisation
Toan Nguyen, Kien Do, Duc Thanh Nguyen, Bao Duong, Thin Nguyen
Strong Generalization Causal Inference Style Transfer Distribution Generalization Unobserved Confounders

December 4, 2022

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu
Deep Learning Generative Adversarial Network GAN Model Unobserved Confounders Privacy Preserving Image Text Based Confounders

November 29, 2022

November 18, 2022

Confounder Balancing for Instrumental Variable Regression with Latent Variable
Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu
Latent Variable Unobserved Confounders Instrumental Variable

October 28, 2022

Mitigating Health Disparities in EHR via Deconfounder
Zheng Liu, Xiaohan Li, Philip Yu
Electronic Health Record Unobserved Confounders Health Disparity Latent Confounders

October 15, 2022

Distributionally Robust Causal Inference with Observational Data
Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
Observational Data Unobserved Confounders Marginal Sensitivity Model Robust Causal

October 11, 2022

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra, Mounia Lalmas
Causal Inference Speech Presence Heterogeneous Treatment Effect Unobserved Confounders Health Intervention Tiny Set

September 15, 2022

$\rho$-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders
Sourabh Balgi, Jose M. Peña, Adel Daoud
Causal Inference Causal Effect Unobserved Confounders Sensitivity Analysis Average Causal Effect Gaussian Copula Graphical Normalizing Flow