Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery

• URL: http://arxiv.org/abs/2301.07427v1
• Date: Wed, 18 Jan 2023 10:54:06 GMT
• Title: Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
• Authors: Martina Cinquini, Fosca Giannotti, Riccardo Guidotti
• Abstract summary: In software testing, data privacy, imbalanced learning, and artificial intelligence explanation, it is crucial to generate plausible data samples. A common assumption of approaches widely used for data generation is the independence of the features. We propose a synthetic dataset generator that can discover nonlinear causalities among the variables and use them at generation time.
• Score: 11.81479419498206
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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