  Most of the current explainability techniques focus on capturing the
importance of features in input space. However, given the complexity of models
and data-generating processes, the resulting explanations are far from being
`complete', in that they lack an indication of feature interactions and
visualization of their `effect'. In this work, we propose a novel
twin-surrogate explainability framework to explain the decisions made by any
CNN-based image classifier (irrespective of the architecture). For this, we
first disentangle latent features from the classifier, followed by aligning
these features to observed/human-defined `context' features. These aligned
features form semantically meaningful concepts that are used for extracting a
causal graph depicting the `perceived' data-generating process, describing the
inter- and intra-feature interactions between unobserved latent features and
observed `context' features. This causal graph serves as a global model from
which local explanations of different forms can be extracted. Specifically, we
provide a generator to visualize the `effect' of interactions among features in
latent space and draw feature importance therefrom as local explanations. Our
framework utilizes adversarial knowledge distillation to faithfully learn a
representation from the classifiers' latent space and use it for extracting
visual explanations. We use the styleGAN-v2 architecture with an additional
regularization term to enforce disentanglement and alignment. We demonstrate
and evaluate explanations obtained with our framework on Morpho-MNIST and on
the FFHQ human faces dataset. Our framework is available at
\url{https://github.com/koriavinash1/GLANCE-Explanations}.
