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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.
A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"
Microarray analysis with Bayesian hierarchical clustering and Bayesian network clustering on three microarray datasets. Pearson's correlation coefficient and an augmented Markov blanket are used for feature selection.
Estimates travel demand and traffic in Andorra based on combination of telecom data and traffic counts using a Gaussian Bayesian Network Model. JS front end maps the results over time.
A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".