CAUSAL INFERENCE AND DISCOVERY IN PYTHON

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

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Key Features:

Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more
Discover modern causal inference techniques for average and heterogenous treatment effect estimation
Explore and leverage traditional and modern causal discovery methods