Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery

TL;DR

This paper introduces a novel method for generating synthetic tabular data that incorporates nonlinear causal relationships among features, improving plausibility and utility in various AI applications.

Contribution

It presents an efficient framework that discovers nonlinear causalities among features using pattern mining, enhancing synthetic data generation accuracy.

Findings

Effective discovery of nonlinear causalities in synthetic data

Improved plausibility of generated datasets

Validated on synthetic and real datasets with known causalities

Abstract

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.