  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.
