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Found 4 papers, 2 papers with code
Learning Gaussian Graphical Models with Latent Confounders
In this paper, we compare and contrast two strategies for inference in graphical models with latent confounders: Gaussian graphical models with latent variables (LVGGM) and PCA-based removal of confounding (PCA+GGM).
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments.
Causal Identification
Causal Inference
Methodology
Shared Subspace Models for Multi-Group Covariance Estimation
We develop a model-based method for evaluating heterogeneity among several p x p covariance matrices in the large p, small n setting.
Methodology
