CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research

TL;DR

CausalPlayground is a Python library that standardizes the generation and sharing of structural causal models, facilitating more consistent and efficient research in causality by providing fine-grained control and RL integration.

Contribution

It introduces a comprehensive, standardized platform for causal data generation, addressing limitations of existing tools and enabling online interaction with SCMs.

Findings

Provides fine-grained control over SCMs and interventions

Enables online interaction with SCMs via Gymnasium

Facilitates more efficient and comparable causality research

Abstract

Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always meet all requirements for state-of-the-art research, ad-hoc methods are often employed. This leads to heterogeneity among datasets and delays research progress. We address the shortcomings of current data-generating libraries by introducing CausalPlayground, a Python library that provides a standardized platform for generating, sampling, and sharing structural causal models (SCMs). CausalPlayground offers fine-grained control over SCMs, interventions, and the generation of datasets of SCMs for learning and quantitative research. Furthermore, by integrating with Gymnasium, the standard framework for reinforcement learning (RL) environments, we enable online interaction with the SCMs. Overall, by introducing CausalPlayground we aim to foster more efficient and comparable research in the field. All code and API documentation is available at https://github.com/sa-and/CausalPlayground.