Dowhy

* Preview for the new functional API (see [notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_functional_api.ipynb)). The new API (in experimental stage) allows for a modular use of the different functionalities and includes separate fit and estimate methods for causal estimators. Please leave your feedback [here](https://github.com/py-why/dowhy/discussions/779). The old DoWhy API based on CausalModel should work as before. (andresmor-ms)

* Faster, better sensitivity analyses.
* Many refutations now support joblib for parallel processing and show a progress bar (astoeffelbauer, yemaedahrav).
* Non-linear sensitivity analysis [ [`Chernozhukov, Cinelli, Newey, Sharma & Syrgkanis (2021)](https://arxiv.org/abs/2112.13398), [example notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb) ] (anusha0409)
* E-value sensitivity analysis [ [Ding & Vanderweele (2016)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820664/), [example notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/sensitivity_analysis_testing.ipynb)] (jlgleason)

* New API for [unit change attribution](https://www.pywhy.org/dowhy/v0.9/dowhy.gcm.html#dowhy.gcm.unit_change.unit_change) (kailashbuki)

* New quality option [`BEST` for auto-assignment](https://www.pywhy.org/dowhy/v0.9/dowhy.gcm.html#module-dowhy.gcm.auto) of causal mechanisms, which uses the optional auto-ML library [AutoGluon](https://auto.gluon.ai/) (bloebp)

* Better conditional independence tests through the [causal-learn](https://github.com/cmu-phil/causal-learn) package (bloebp)

* Algorithms for computing efficient backdoor sets [ [example notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_efficient_backdoor_example.ipynb) ] (esmucler)

* Support for estimating controlled direct effect (amit-sharma)

* Support for multi-valued treatments for econml estimators (EgorKraevTransferwise)

* New PyData theme for [documentation](https://www.pywhy.org/dowhy/) with new homepage, Getting started guide, revised User Guide and examples page (petergtz)

* A [contributing guide](https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst) and simplified instructions for new contributors (MichaelMarien)

* Streamlined dev environment using Poetry for managing dependencies and project builds (darthtrevino)

* Bug fixes

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