Inflation: a Python library for classical and quantum causal compatibility

TL;DR

Inflation is a Python library that helps determine if observed data can be explained by classical or quantum causal models, aiding research in causality and quantum nonlocality.

Contribution

The paper introduces Inflation, a versatile and user-friendly Python toolkit for causal compatibility analysis in classical and quantum settings.

Findings

Supports pure causal compatibility problems

Enables optimization over sets of compatible correlations

Flexible and extensible design for custom analysis

Abstract

We introduce Inflation, a Python library for assessing whether an observed probability distribution is compatible with a causal explanation. This is a central problem in both theoretical and applied sciences, which has recently witnessed significant advances from the area of quantum nonlocality, namely, in the development of inflation techniques. Inflation is an extensible toolkit that is capable of solving pure causal compatibility problems and optimization over (relaxations of) sets of compatible correlations in both the classical and quantum paradigms. The library is designed to be modular and with the ability of being ready-to-use, while keeping an easy access to low-level objects for custom modifications.