DoWhy

A causal Inference Library from Microsoft

DoWhy is a Python Library for Causal Inference from Microsoft, developed by Amit Sharma, Emre Kiciman. Its name is inspired by Judea Pearl’s do-calculus for causal inference.

About

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. DoWhy stresses on Interpretability of the output. At any point in the analysis, you can inspect the untested assumptions.

DoWhy uses the Bayesian graphical Model framework where one can specify all the assumptions about the data generating process.

DoWhy is a very effective and helpful library for implementing Causal Inference. The library implements causality by first making the underlying assumptions explicit, for example, by explicitly representing identified estimands. Secondly by making sensitivity analysis and other robustness checks on these explicit assumptions, for example, by introducing a confounder or by replacing the intervention with placebo. The goal here is to enable people to focus their efforts on identifying assumptions for causal inference, rather than on details of estimation.

DoWhy examples: Github

Resource:

1. https://github.com/Microsoft/dowhy

2. https://microsoft.github.io/dowhy/