Inflation: a Python library for classical and quantum causal compatibility

Emanuel-Cristian Boghiu1, Elie Wolfe2, and Alejandro Pozas-Kerstjens3

1ICFO – Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
2Perimeter Institute for Theoretical Physics, 31 Caroline St. N., Waterloo, Ontario, Canada, N2L 2Y5
3Instituto de Ciencias Matemáticas (CSIC-UAM-UC3M-UCM), 28049 Madrid, Spain

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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.

One of the main challenges in science is identifying which are the causes behind some observed correlations. Is a vaccine effective against a disease? Does raising salaries encourage spending? All these questions can be formulated analyzed using the tools of causal inference, but are often numerically hard to answer. Recently, new tools have appeared in the field of quantum nonlocality, called inflation methods, that allow to relax these hard problems to numerically tractable ones. In this work we present a Python package that implements such methods.

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