Causal Inference 360 Open Source Toolkit

Our goal is to help guide data scientists who wish to move beyond observing differences (descriptive statistics) to quantifying cause-and-effect relationships in data. We also want to make it easy for machine learning practitioners who are shifting from solving prediction problems to asking what-if questions for decision-making support.

This open source Python toolkit is designed to bring long-standing machine-learning methodologies to the field of causal inference. We offer a set of established causal inference methods, along with some new ones, that can help you train causal models. It also includes a set of evaluation methods that let you select the correct method from the toolkit, choose the correct underlying model, and perform parameter tuning.

Our unique contribution lies not only in combining multiple causal inference models into one package, but also in adapting well-established machine-learning validation methodology to the context of causal inference, and adding a few causal-specific ones. The combination of multiple methods and the means to evaluate them is your key to building strong causal inference models that can be tested for reliability, consistency, and robustness.

Using our toolkit, you can now easily train causal models that estimate the effect of an intervention on an outcome. The models you use can be as complex or as simple as your problem and your data requires. We invite you to use the toolkit and to help us improve it.

If you use this package, please reference Shimoni et at., 2019