DoWhy
v0.5.1

Introducing DoWhy

  • DoWhy | An end-to-end library for causal inference
  • Graphical Models and Potential Outcomes: Best of both worlds
  • Four steps of causal inference
  • Citing this package
  • Roadmap
  • Contributing

Quick-Start Tutorial

  • Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)

Starter Notebooks

  • Getting started with DoWhy: A simple example
  • Confounding Example: Finding causal effects from observed data
  • DoWhy: Different estimation methods for causal inference
  • Simple example on using Instrumental Variables method for estimation
  • Different ways to load an input graph
  • Demo for the DoWhy causal API
  • Do-sampler Introduction

Case Study Notebooks

  • DoWhy-The Causal Story Behind Hotel Booking Cancellations
  • Estimating the effect of a Member Rewards program
  • DoWhy example on ihdp (Infant Health and Development Program) dataset
  • DoWhy example on the Lalonde dataset
  • Applying refutation tests to the Lalonde and IHDP datasets
  • Lalonde Pandas API Example

Advanced Notebooks

  • Conditional Average Treatment Effects (CATE) with DoWhy and EconML
  • Mediation analysis with DoWhy: Direct and Indirect Effects
  • A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions
  • Estimating effect of multiple treatments
  • Iterating over multiple refutation tests

Package

  • Code repository & Versions
  • dowhy package
DoWhy
  • »
  • Search

© Copyright 2022, PyWhy contributors.

Built with Sphinx using a theme provided by Read the Docs.
Other Versions v: v0.5.1
Releases
v0.1.1-alpha
v0.2
v0.4
v0.5
v0.5.1
v0.6
v0.7
v0.7.1
v0.8
Branches
main