pyparcs 0.1.2

A simulation package for causal methods

PARCS: a Python Package for Causal Simulation

PA-rtially R-andomized C-ausal S-imulator is a simulation tool for causal methods. This library is designed to facilitate simulation study design and serve as a standard benchmarking tool for causal inference and discovery methods. PARCS generates simulation mechanisms based on causal DAGs and a wide range of adjustable parameters. Once the simulation setup is described via legible instructions and rules, PARCS automatically probes the space of all complying mechanisms and synthesizes data from both observational and interventional distributions.

For a complete introduction and documentation, please read the docs from the docs folder. Sphinx make is needed. Doc website will be launched soon.

NOTE: The corresponding research paper will be announced here for citation and reference.