  Discovering causal models from observational and interventional data is an
important first step preceding what-if analysis or counterfactual reasoning. As
has been shown before, the direction of pairwise causal relations can, under
certain conditions, be inferred from observational data via standard
gradient-boosted classifiers (GBC) using carefully engineered statistical
features. In this paper we apply deep convolutional neural networks (CNNs) to
this problem by plotting attribute pairs as 2-D scatter plots that are fed to
the CNN as images. We evaluate our approach on the 'Cause- Effect Pairs' NIPS
2013 Data Challenge. We observe that a weighted ensemble of CNN with the
earlier GBC approach yields significant improvement. Further, we observe that
when less training data is available, our approach performs better than the GBC
based approach suggesting that CNN models pre-trained to determine the
direction of pairwise causal direction could have wider applicability in causal
discovery and enabling what-if or counterfactual analysis.
