DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

TL;DR

DagSim is a flexible Python framework that enables DAG-based data simulation with unconstrained variable types and relations, promoting transparency and modularity for complex data scenarios.

Contribution

It introduces a novel DAG-based simulation framework that supports arbitrary variable types and functional relations, overcoming limitations of existing methods.

Findings

Supports complex variable types and relations

Uses a transparent YAML model specification

Demonstrates applications in image and bio-sequence data

Abstract

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.