# Partially Linear Additive Gaussian Graphical Models

arXiv: https://arxiv.org/abs/1906.03362 · HTML: https://mlsanity.com/paper/1906.03362/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

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## Abstract (arXiv)

We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an L1-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove √n-sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.