DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation
This provides a modular and transparent tool for researchers in machine learning and causal inference to simulate complex data scenarios, though it is incremental as it builds on existing DAG-based methods.
The authors tackled the limitation of DAG-based simulation frameworks being confined to simple variable types and functional forms by developing DagSim, a Python framework that supports unconstrained data types and relations, enabling flexible simulation of complex data like images and bio-sequences.
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences.