  In this paper we present a comprehensive view of prominent causal discovery
algorithms, categorized into two main categories (1) assuming acyclic and no
latent variables, and (2) allowing both cycles and latent variables, along with
experimental results comparing them from three perspectives: (a) structural
accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
inference. For (b) and (c) we train causal Bayesian networks with structures as
predicted by each causal discovery technique to carry out counterfactual or
standard predictive inference. We compare causal algorithms on two pub- licly
available and one simulated datasets having different sample sizes: small,
medium and large. Experiments show that structural accuracy of a technique does
not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
surveyed structure learning algorithms do not perform well in terms of
structural accuracy in case of datasets having large number of variables.
