Remove 'experimental' disclaimer from GCM modules

Also slightly change citation hint.

Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>

README.rst

@@ -54,7 +54,7 @@ News
   redirect your git command for cloning, pulling, etc., we recommend updating git remotes and bookmarks. Please note
   that the **documentation** has now moved to https://py-why.github.io/dowhy with **no** redirect from the old URL.
 
-* **Experimental support for GCM-based inference**
+* **Support for GCM-based inference**
 
   We have started adding support for graphical causal model-based inference (or in short GCM-based). At the moment,
   this includes support for interventions, counterfactuals, and attributing distribution changes. As part of this,
@@ -203,10 +203,10 @@ estimate (if any). Here's a sample output of the linear regression estimator.
 For a full code example, check out the `Getting Started with DoWhy <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from other libraries such as EconML and CausalML, as shown in the `Conditional Treatment Effects <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. For more examples of using DoWhy, check out the Jupyter notebooks in `docs/source/example_notebooks <https://github.com/microsoft/dowhy/tree/main/docs/source/example_notebooks/>`_ or try them online at `Binder <https://mybinder.org/v2/gh/microsoft/dowhy/main?filepath=docs%2Fsource%2F>`_.
 
 
-GCM-based inference (experimental)
+GCM-based inference
 ----------------------------------
 
-Graphical causal model-based inference, or GCM-based inference for short, is an experimental addition to DoWhy. For
+Graphical causal model-based inference, or GCM-based inference for short, is an addition to DoWhy. For
 details, check out the `documentation for the gcm sub-package <https://py-why.github.io/dowhy/main/user_guide/gcm_based_inference/index.html>`_. The basic
 recipe for this API works as follows:
 
@@ -497,7 +497,7 @@ As a practical example, `this notebook <https://github.com/microsoft/dowhy/blob/
 
 Citing this package
 ====================
-If you find DoWhy useful for your work, please cite the following two references:
+If you find DoWhy useful for your work, please cite **both** of the following two references:
 
 - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216
 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821

docs/source/cite.rst

@@ -1,7 +1,7 @@
 Citing this package
 -------------------
 
-If you find DoWhy useful for your work, please cite the following two references:
+If you find DoWhy useful for your work, please cite **both** of the following two references:
 
 - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216
 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821

dowhy/gcm/__init__.py

@@ -1,7 +1,4 @@
-"""The gcm sub-package provides features built on top of graphical causal model (GCM) based inference. The status of
-this addition and its API is considered experimental, meaning there might be breaking changes to its API in the
-future.
-"""
+"""The gcm sub-package provides features built on top of graphical causal model (GCM) based inference."""
 
 from . import auto, config, divergence, ml, shapley, stats, uncertainty, util
 from .anomaly import anomaly_scores, attribute_anomalies

dowhy/gcm/anomaly_scorers.py

@@ -1,8 +1,4 @@
-"""This module contains implementations of different anomaly scorers.
-
-Classes and functions in this module should be considered experimental, meaning there might be breaking API changes in
-the future.
-"""
+"""This module contains implementations of different anomaly scorers."""
 
 from typing import Optional
 

dowhy/gcm/causal_mechanisms.py

@@ -1,7 +1,4 @@
-"""This module implements different causal mechanisms.
-
-Classes in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module implements different causal mechanisms."""
 
 import copy
 from abc import ABC, abstractmethod

dowhy/gcm/causal_models.py

@@ -1,7 +1,4 @@
-"""This module defines the fundamental classes for graphical causal models (GCMs).
-
-Classes in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module defines the fundamental classes for graphical causal models (GCMs)."""
 
 from typing import Any, Callable, Optional, Union
 

dowhy/gcm/confidence_intervals.py

@@ -1,7 +1,4 @@
-"""This module provides functionality to estimate confidence intervals via bootstrapping.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functionality to estimate confidence intervals via bootstrapping."""
 
 from typing import Any, Callable, Dict, List, Tuple, Union
 

dowhy/gcm/confidence_intervals_cms.py

@@ -1,7 +1,4 @@
-"""This module provides functionality to estimate confidence intervals via bootstrapping the fitting and sampling.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functionality to estimate confidence intervals via bootstrapping the fitting and sampling."""
 
 from functools import partial
 from typing import Any, Callable, Dict, Optional, Union

dowhy/gcm/density_estimators.py

@@ -1,8 +1,4 @@
-"""This module contains implementations of different density estimators.
-
-Classes and functions in this module should be considered experimental, meaning there might be breaking API changes in
-the future.
-"""
+"""This module contains implementations of different density estimators."""
 
 from typing import Optional
 

dowhy/gcm/distribution_change.py

@@ -1,7 +1,4 @@
-"""This module defines functions to attribute distribution changes.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module defines functions to attribute distribution changes."""
 
 import logging
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union

dowhy/gcm/divergence.py

@@ -1,6 +1,3 @@
-"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the
-future.
-"""
 from functools import partial
 from typing import Callable, Union
 

dowhy/gcm/falsify.py

@@ -1,7 +1,4 @@
-"""This module provides functionality to falsify a user-given DAG given observed data.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functionality to falsify a user-given DAG given observed data."""
 import warnings
 from dataclasses import dataclass, field
 from enum import Enum, auto

dowhy/gcm/feature_relevance.py

@@ -1,10 +1,7 @@
 """This module allows to estimate the feature relevance of inputs with respect to a given model. While these models can
 be blackbox prediction models, it is also possible to explain causal mechanisms with respect to the direct parents.
 In these cases, it would be possible to incorporate the noise to represent the part of the generation process that
-cannot be explained by the parents.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+cannot be explained by the parents."""
 from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import numpy as np

dowhy/gcm/fitting_sampling.py

@@ -1,7 +1,4 @@
-"""This module provides functionality for fitting probabilistic causal models and drawing samples from them.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functionality for fitting probabilistic causal models and drawing samples from them."""
 
 from typing import Any
 

dowhy/gcm/independence_test/kernel.py

@@ -1,6 +1,3 @@
-"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the
-future.
-"""
 from typing import Callable, List, Optional, Union
 
 import numpy as np

dowhy/gcm/independence_test/kernel_operation.py

@@ -1,7 +1,3 @@
-"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the
-future.
-"""
-
 from typing import Optional
 
 import numpy as np

dowhy/gcm/influence.py

@@ -1,7 +1,4 @@
-"""This module provides functions to estimate causal influences.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functions to estimate causal influences."""
 import logging
 import warnings
 from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast

dowhy/gcm/ml/classification.py

@@ -1,6 +1,3 @@
-"""Functions and classes in this module should be considered experimental, meaning there might be breaking API changes
-in the future.
-"""
 from abc import abstractmethod
 from typing import List
 

dowhy/gcm/ml/regression.py

@@ -1,6 +1,3 @@
-"""Functions and classes in this module should be considered experimental, meaning there might be breaking API changes
-in the future.
-"""
 from abc import abstractmethod
 from typing import Any
 

dowhy/gcm/shapley.py

@@ -1,8 +1,4 @@
-"""This module provides functionality for shapley value estimation.
-
-Classes and functions in this module should be considered experimental, meaning there might be breaking API changes in
-the future.
-"""
+"""This module provides functionality for shapley value estimation."""
 
 import itertools
 import math

dowhy/gcm/stats.py

@@ -1,7 +1,3 @@
-"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the
-future.
-"""
-
 from typing import Callable, List, Optional, Union
 
 import numpy as np

dowhy/gcm/stochastic_models.py

@@ -1,7 +1,4 @@
-"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`.
-
-Classes in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`."""
 
 import warnings
 from typing import Dict, Optional, Tuple, Union

dowhy/gcm/uncertainty.py

@@ -1,7 +1,4 @@
-"""Functions to estimate uncertainties such as entropy, KL divergence etc.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""Functions to estimate uncertainties such as entropy, KL divergence etc."""
 
 import numpy as np
 from numpy.linalg import det

dowhy/gcm/util/general.py

@@ -1,7 +1,3 @@
-"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the
-future.
-"""
-
 import random
 from typing import Dict, Optional, Union
 

dowhy/gcm/validation.py

@@ -1,8 +1,4 @@
-"""Contains a method to reject the causal graph and validate causal mechanisms such as post non-linear models.
-
-Classes and functions in this module should be considered experimental, meaning there might be breaking API changes in
-the future.
-"""
+"""Contains a method to reject the causal graph and validate causal mechanisms such as post non-linear models."""
 from enum import Enum, auto
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 

dowhy/gcm/whatif.py

@@ -1,7 +1,4 @@
-"""This module provides functionality to answer what-if questions.
-
-Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
-"""
+"""This module provides functionality to answer what-if questions."""
 
 from typing import Any, Callable, Dict, Iterable, List, Optional, Union