|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ |gurubase|_
.. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg
.. _PyPiVersion: https://pypi.org/project/dowhy/
.. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg
.. _PythonSupport: https://pypi.org/project/dowhy/
.. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg
.. _BuildStatus: https://github.com/py-why/dowhy/actions
.. |Downloads| image:: https://pepy.tech/badge/dowhy
.. _Downloads: https://pepy.tech/project/dowhy
.. |discord| image:: https://img.shields.io/discord/818456847551168542
.. _discord: https://discord.gg/cSBGb3vsZb
.. |gurubase| image:: https://img.shields.io/badge/Gurubase-Ask%20DoWhy%20Guru-006BFF
.. _gurubase: https://gurubase.io/g/dowhy
.. image:: dowhy-logo-large.png
:width: 50%
:align: center
Checkout the documentation <https://py-why.github.io/dowhy/>_
- The documentation, user guide, sample notebooks and other information are available at
https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>_
- DoWhy is part of the
PyWhy Ecosystem <https://www.pywhy.org/>. For more tools and libraries related to causality, checkout the PyWhy GitHub organization <https://github.com/py-why/>!
- For any questions, comments, or discussions about specific use cases, join our community on
Discord <https://discord.gg/cSBGb3vsZb>_ (|discord|_)
- Jump right into some case studies:
- Effect estimation:
Hotel booking cancellations <https://medium.com/data-science/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>_ | Effect of customer loyalty programs <https://www.pywhy.org/dowhy/main/example_notebooks/dowhy_example_effect_of_memberrewards_program.html>_ | Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>_ | Effect of home visits on infant health (IHDP) <https://medium.com/data-science/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>_ | Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>_
- Root cause analysis and explanations:
Causal attribution and root-cause analysis of an online shop <https://www.pywhy.org/dowhy/main/example_notebooks/gcm_online_shop.html>_ | Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://www.pywhy.org/dowhy/main/example_notebooks/gcm_rca_microservice_architecture.html>_ | Finding Root Causes of Changes in a Supply Chain <https://www.pywhy.org/dowhy/main/example_notebooks/gcm_supply_chain_dist_change.html>_
For more example notebooks, see here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>_
Introduction & Key Features
Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions.
DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification
of causal influences, diagnosis of causal structures, root cause analysis, interventions and
counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method,
thus making inference more robust and accessible to non-experts.
Graphical Causal Models and Potential Outcomes: Best of both worlds
DoWhy builds on two of the most powerful frameworks for causal inference:
graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for
modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based
primarily on potential outcomes.
For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data
generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute
observed effects to particular variables or estimate point-wise counterfactuals.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>_
We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018 <http://www.kdd.org/kdd2018/>) conference: causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>.
For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>_ and for an introduction to the graphical causal model API, see the PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>_.
여기까지가 README 앞부분입니다. 나머지는 원문에서 읽을 수 있습니다. README 전문 보기 ↗
