Dowhy

* New feature allowing users to write equations for the DGP of each node and obtain a causal model back with the mechanisms assigned (1106 )
* Convenience function to access fitted estimator instances from CausalModel (1113 )
* Bug fixes in Kernel-based independence test and networkx plot function
* Bug fixes for confidence intervals and regressionestimator
* Some improvements to CI/CD (auto-check readme on each PR, updated package publishing process, fix for timeout error)

Contributors: bhatt-priyadutt, drawlinson, bloebp, amit-sharma

* New functional API is ready for use. Try out the [notebook](https://www.pywhy.org/dowhy/v0.11/example_notebooks/dowhy_functional_api.html)
* A [notebook](https://www.pywhy.org/dowhy/v0.11/example_notebooks/dowhy_causal_discovery_example.html) showing how to use causal-learn graph discovery with DoWhy
* New [notebook](https://www.pywhy.org/dowhy/v0.11/example_notebooks/gcm_icc.html) demonstrating use of the intrinsic causal influence feature
* Enhanced compatibility between GCM and CausalModel api
* Frontdoor identification now supports multiple variables
* New [module](https://www.pywhy.org/dowhy/v0.11/user_guide/modeling_gcm/model_evaluation.html) for evaluating performance and falsifying assumptions of GCM models
* GCM auto assignment now returns a summary
* Extended documentation, revised and simpler README
* Bug fixes and improvements

A big thank you to all the contributors: amit-sharma, bloebp, kunwuz

This is a patch release.
* Added support for exposing interventional outcomes (drawlinson)
* Fixed bugs for pandas 2.0 support (bloebp) and confidence value for statistical test (amit-sharma)
* Additions to invariant nodes in GCM (bhatt-priyadutt)
* Fixing release pipeline (kbattocchi)

Thanks to everyone for contributing issues and fixes for this patch.

* Introducing an updated **[user guide](https://www.pywhy.org/dowhy/main/user_guide/intro.html)** for navigating the world of causality. The user guide is a great resource to learn about the different causal tasks, which ones may be relevant for you, and how to implement them using DoWhy.
* **Causal prediction** is the latest task supported by DoWhy! Try out the [prediction notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb) by jivatneet
* A new technique for **validating causal graphs**. Check out the [notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_falsify_dag.ipynb) by eeulig
* **New refutation**: Overrule for learning boolean rules to describe support of the data/overlap between treatment and control groups in the data. Check out the [notebook](https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_refuter_assess_overlap.ipynb) by moberst
* Added a new method to estimate intrinsic causal influences for a single sample.
* Refactor of estimator API that allows separate fit and estimate methods
* Several optimizations and speed-ups of GCM methods
* Python 3.11 support and a simpler dependency list

A big thanks to all the contributors. AlxndrMlk amit-sharma andresmor-ms bloebp darthtrevino eeulig eltociear emrekiciman jivatneet kbattocchi Klesel MFreidank MichaelMarien moberst Padarn petergtz RoseDeSicilia26 sgrimbly vspinu yoshiakifukushima Zethson

Minor update to v0.9.

* Python 3.10 support
* Streamlined dependency structure for the dowhy package (fewer required dependencies)
* Color option for plots (eeulig)

Thanks darthtrevino, petergtz, andresmor-ms for driving this release!

* 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