  Synthetic data generation has been widely adopted in software testing, data
privacy, imbalanced learning, and artificial intelligence explanation. In all
such contexts, it is crucial to generate plausible data samples. A common
assumption of approaches widely used for data generation is the independence of
the features. However, typically, the variables of a dataset depend on one
another, and these dependencies are not considered in data generation leading
to the creation of implausible records. The main problem is that dependencies
among variables are typically unknown. In this paper, we design a synthetic
dataset generator for tabular data that can discover nonlinear causalities
among the variables and use them at generation time. State-of-the-art methods
for nonlinear causal discovery are typically inefficient. We boost them by
restricting the causal discovery among the features appearing in the frequent
patterns efficiently retrieved by a pattern mining algorithm. We design a
framework for generating synthetic datasets with known causalities to validate
our proposal. Broad experimentation on many synthetic and real datasets with
known causalities shows the effectiveness of the proposed method.
