DoWhy documentation
Date: Dec 17, 2022 Version: v0.9.1
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Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark
causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal
structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals.
Getting started
New to DoWhy? Our Getting started guide will get you up to speed in minutes. It’ll help you install DoWhy and
write your first lines of code. Once completed, you’ll be ready to the run examples and follow along in the
User Guide.
User Guide
Complete newbie when it comes to causal inference and DoWhy? Then you probably want to read our
comprehensive User Guide. It guides you through everything you need to know, including the concepts and
science you need to know when trying to solve non-trivial problems.
Examples
If you prefer to learn by example, we recommend to browse the examples. It covers a wide variety of problems
that you can use to liken to your own problem.
API Reference
The API reference contains a detailed description of the functions, modules, and objects included in DoWhy.
It assumes that you have an understanding of the key concepts.