  Detecting conditional independencies plays a key role in several statistical
and machine learning tasks, especially in causal discovery algorithms. In this
study, we introduce LCIT (Latent representation based Conditional Independence
Test)-a novel non-parametric method for conditional independence testing based
on representation learning. Our main contribution involves proposing a
generative framework in which to test for the independence between X and Y
given Z, we first learn to infer the latent representations of target variables
X and Y that contain no information about the conditioning variable Z. The
latent variables are then investigated for any significant remaining
dependencies, which can be performed using the conventional partial correlation
test. The empirical evaluations show that LCIT outperforms several
state-of-the-art baselines consistently under different evaluation metrics, and
is able to adapt really well to both non-linear and high-dimensional settings
on a diverse collection of synthetic and real data sets.
