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DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Implementations of var-sortability, sortnregress, and chain-orientation as presented in the article "Beware of the Simulated DAG": https://arxiv.org/abs/2102.13647.
ImpactFlow is a Python Library for decision modeling based on causal decision models - in which levers and external factors of decisions feed into outcomes.
We perform market regime detection by testing three deep representation learning models tailored to the SPD Riemannian manifold of correlation matrices constructed from Bloomberg JSE Top 60 traded stock price returns data and synthetically-generated block hierarchical correlation matrices.