  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 $L_1$-regularized maximal
pseudo-profile likelihood estimator (MaPPLE) for which we prove
$\sqrt{n}$-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.

Machine Learning;Machine Learning