Inflation: Python implementations of the Inflation Technique for causal inference

Inflation is a Python package that implements inflation algorithms for causal inference. In causal inference, the main task is to determine which causal relationships can exist between different observed random variables. Inflation algorithms are a class of techniques designed to solve the causal compatibility problem, that is, test compatibility between some observed data and a given causal relationship.

The first version of this package implements the inflation technique for quantum causal compatibility. For details, see Physical Review X 11 (2), 021043 (2021). The inflation technique for classical causal compatibility will be implemented in a future update.

Examples of use of this package include:

• Feasibility problems and extraction of certificates.
• Optimization of Bell operators.
• Optimisation over classical distributions.
• Standard Navascués-Pironio-Acín hierarchy.
• Scenarios with partial information.