Title: Data-driven causal path discovery without prior knowledge - a benchmark study,
Abstract: Causal discovery broadens the inference possibilities, as correlation does
not inform about the relationship direction. The common approaches were
proposed for cases in which prior knowledge is desired, when the impact of a
treatment/intervention variable is discovered or to analyze time-related
dependencies. In some practical applications, more universal techniques are
needed and have already been presented. Therefore, the aim of the study was to
assess the accuracies in determining causal paths in a dataset without
considering the ground truth and the contextual information. This benchmark was
performed on the database with cause-effect pairs, using a framework consisting
of generalized correlations (GC), kernel regression gradients (GR) and absolute
residuals criteria (AR), along with causal additive modeling (CAM). The best
overall accuracy, 80%, was achieved for the (majority voting) combination of
GC, AR, and CAM, however, the most similar sensitivity and specificity values
were obtained for AR. Bootstrap simulation established the probability of
correct causal path determination (which pairs should remain indeterminate).
The mean accuracy was then improved to 83% for the selected subset of pairs.
The described approach can be used for preliminary dependence assessment, as an
initial step for commonly used causality assessment frameworks or for
comparison with prior assumptions.