dowhy
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.
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py-why
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Software by py-why
EconML
EconML is a Python package designed to estimate heterogeneous treatment effects from observational data using machine learning. Developed as part of Microsoft Research's ALICE project, it combines state-of-the-art machine learning techniques with econometrics to automate complex causal inference problems. The toolkit measures the causal effect of treatment variables on outcome variables while controlling for observed features, allowing users to determine how these effects vary across different subpopulations. EconML supports a wide range of modeling techniques including random forests, boosting, lasso, and neural networks, all within a unified API that preserves causal interpretation. It handles various experimental and observational scenarios, accommodating users with access to instrumental variables or assuming no unobserved confounders. Key features include the implementation of orthogonal machine learning algorithms like Double Machine Learning, provision of valid confidence intervals and inference result