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🐜 Add acknowledgements section
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docs/user_guide/index.rst

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.. meta::
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:description lang=en:
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User guide for uplift modeling and casual inference.
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.. _user_guide:
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**********
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User guide
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**********
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.. image:: https://habrastorage.org/webt/hf/7i/nu/hf7inuu3agtnwl1yo0g--mznzno.jpeg
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:alt: User guide for uplift modeling and casual inference.
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:alt: User guide for uplift modeling and causal inference.
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Uplift modeling evaluates the effect of communication with clients and selects the group that is most affected.
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This class of tasks is easy to implement, but it is not widely used in the machine learning courses or in the relevant literature.
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In this guide we will try to reveal all the necessary basics for modeling uplift.
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Uplift modeling estimates the effect of communication action on some customer outcome and gives an opportunity to efficiently target customers which are most likely to respond to a marketing campaign.
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It is relatively easy to implement, but surprisingly poorly covered in the machine learning courses and literature.
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This guide is going to shed some light on the essentials of causal inference estimating and uplift modeling.
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.. toctree::
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:maxdepth: 3
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- `Irina Elisova <https://github.com/ElisovaIra>`_
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- `Maksim Shevchenko <https://github.com/maks-sh>`_
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**Graphics:**
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**Acknowledgements:**
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- `Alina Zhukova <https://www.linkedin.com/in/alina-zhukova-a307b677/>`_ - pictures on the main User Guide page and on the "Type of clients" pages.
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- `Kirill Liksakov <https://github.com/kirrlix1994>`_ - uplift metrics research
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- `Alina Zhukova <https://www.linkedin.com/in/alina-zhukova-a307b677/>`_ - artwork: user guide cover and key pictures
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Citations
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----------
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html}}
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}
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}

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