Dowhy

A big thanks to petergtz, kailashbuki, and bloebp for the GCM package and anusha0409 for an implementation of partial R2 sensitivity analysis for linear models.

* **Graphical Causal Models:** SCMs, root-cause analysis, attribution, what-if analysis, and more.

* **Sensitivity Analysis:** Faster, more general partial-R2 based sensitivity analysis for linear models, based on [Cinelli & Hazlett (2020)](https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12348).

* **New docs structure:** Updated docs structure including user and contributors' guide. Check out the [docs](https://py-why.github.io/dowhy/).

* Bug fixes


Contributors: amit-sharma, anusha0409, bloebp, EgorKraevTransferwise, EliKling, kailashbuki, itsoum, MichaelMarien, petergtz, ryanrussell

* Graph refuter with conditional independence tests to check whether data conforms to the assumed causal graph

* Better docs for estimators by adding the method-specific parameters directly in its own init method
* Support use of custom external estimators
* Consistent calls for init_params for dowhy and econml estimators
* Add support for Dagitty graphs
* Bug fixes for GLM model, causal model with no confounders, and hotel case-study notebook

Thank you EgorKraevTransferwise, ae-foster, and anusha0409 for your contributions!

* **[Major]** Faster backdoor identification with support for minimal adjustment, maximal adjustment
or exhaustive search. More test coverage for identification.

* **[Major]** Added new functionality of causal discovery [Experimental].
DoWhy now supports discovery algorithms from external libraries like CDT.
[Example notebook](https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb)

* **[Major]** Implemented ID algorithm for causal identification. [Experimental]

* Added friendly text-based interpretation for DoWhy's effect estimate.

* Added a new estimation method, distance matching that relies on a distance
metrics between inputs.

* Heuristics to infer default parameters for refuters.

* Inferring default strata automatically for propensity score stratification.

* Added support for custom propensity models in propensity-based estimation
methods.

* Bug fixes for confidence intervals for linear regression. Better version of
bootstrap method.

* Allow effect estimation without need to refit the model for econml estimators

Big thanks to AndrewC19, ha2trinh, siddhanthaldar, and vojavocni

* **[Major]** Placebo refuter now supports instrumental variable methods
* **[Major]** Moved matplotlib to an optional dependency. Can be installed using `pip install dowhy[plotting]`
* **[Major]** A new method for generating unobserved confounder for refutation
* Dummyoutcomerefuter supports unobserved confounder
* Update to align with EconML's new API
* All refuters now support control and treatment values for continuous treatments
* Better logging configuration


A big thanks to arshiaarya, n8sty, moprescu and vojavocni for their contributions!

* Added an optimized version for `identify_effect`
* Fixed a bug for direct and indirect effects computation
* More test coverage: Notebooks are also under automatic tests
* updated conditional-effects-notebook to support the latest EconML version
* EconML metalearners now have the expected behavior: accept both `common_causes` and `effect_modifiers`
* Fixed some bugs in refuter tests

**Installation**
* DoWhy can be installed on Conda now!

**Code**
* Support for identification by mediation formula
* Support for the front-door criterion
* Linear estimation methods for mediation
* Generalized backdoor criterion implementation using paths and d-separation
* Added GLM estimators, including logistic regression
* New API for interpreting causal models, estimates and refuters. First interpreter by ErikHambardzumyan visualizes
how the distribution of confounder changes
* Friendlier error messages for propensity score stratification estimator when there is not enough data in a bin.
* Enhancements to the dummy outcome refuter with machine learned components--now can simulate non-zero effects too. Ready for alpha testing

**Docs**
* New case studies using DoWhy on [hotel booking cancellations](https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/DoWhy-The%20Causal%20Story%20Behind%20Hotel%20Booking%20Cancellations.ipynb) and [membership rewards programs](https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb).
* New [notebook](https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_multiple_treatments.ipynb) on using DoWhy+EconML for estimating effect of multiple treatments
* A [tutorial](https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb) on causal inference using dowhy and econml
* Better organization of docs and notebooks on the documentation website (https://microsoft.github.io/dowhy/)

**Community**
* Created a [contributors page](https://github.com/microsoft/dowhy/blob/master/CONTRIBUTING.md) with guidelines for contributing
* Added allcontributors bot so that new contributors can added just after their pull requests are merged

A big thanks to Tanmay-Kulkarni101, ErikHambardzumyan, Sid-darthvader for their contributions.