References
==========

Open Source Software Projects
-----------------------------

Python Packages
~~~~~~~~~~~~~~~

- `DoWhy <https://github.com/Microsoft/dowhy>`_: a package for causal inference based on causal graphs.
- `CausalLift <https://github.com/Minyus/causallift/>`_: a package for uplift modeling based on T-learner :cite:`kunzel2019metalearners`.
- `PyLift <https://github.com/wayfair/pylift>`_: a package for uplift modeling based on the transformed outcome method in :cite:`athey2016recursive`.
- `EconML <https://github.com/Microsoft/EconML>`_: a package for treatment effect estimation with orthogonal random forest :cite:`oprescu2018orthogonal`, DeepIV :cite:`hartford2017deep` and other ML methods.

R Packages
~~~~~~~~~~

- `uplift <https://cran.r-project.org/web/packages/uplift/index.html>`_: a package for treatment effect estimation with ML.
- `grf <https://github.com/grf-labs/grf>`_: a package for forest-based honest estimation from :cite:`athey2019generalized`.

Papers
------

.. bibliography:: refs.bib
    :style: plain