From cf14caab5c0c9652189892b7cd54311f5d7f9f92 Mon Sep 17 00:00:00 2001 From: Patrick Bloebaum Date: Thu, 9 Nov 2023 16:04:10 -0800 Subject: [PATCH] Remove 'experimental' disclaimer from GCM modules Also slightly change citation hint. Signed-off-by: Patrick Bloebaum --- README.rst | 8 ++++---- docs/source/cite.rst | 2 +- dowhy/gcm/__init__.py | 5 +---- dowhy/gcm/anomaly_scorers.py | 6 +----- dowhy/gcm/causal_mechanisms.py | 5 +---- dowhy/gcm/causal_models.py | 5 +---- dowhy/gcm/confidence_intervals.py | 5 +---- dowhy/gcm/confidence_intervals_cms.py | 5 +---- dowhy/gcm/density_estimators.py | 6 +----- dowhy/gcm/distribution_change.py | 5 +---- dowhy/gcm/divergence.py | 3 --- dowhy/gcm/falsify.py | 5 +---- dowhy/gcm/feature_relevance.py | 5 +---- dowhy/gcm/fitting_sampling.py | 5 +---- dowhy/gcm/independence_test/kernel.py | 3 --- dowhy/gcm/independence_test/kernel_operation.py | 4 ---- dowhy/gcm/influence.py | 5 +---- dowhy/gcm/ml/classification.py | 3 --- dowhy/gcm/ml/regression.py | 3 --- dowhy/gcm/shapley.py | 6 +----- dowhy/gcm/stats.py | 4 ---- dowhy/gcm/stochastic_models.py | 5 +---- dowhy/gcm/uncertainty.py | 5 +---- dowhy/gcm/util/general.py | 4 ---- dowhy/gcm/validation.py | 6 +----- dowhy/gcm/whatif.py | 5 +---- 26 files changed, 22 insertions(+), 101 deletions(-) diff --git a/README.rst b/README.rst index c2b425db6..376278bd3 100755 --- a/README.rst +++ b/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 `_ 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 `_ notebook. For more examples of using DoWhy, check out the Jupyter notebooks in `docs/source/example_notebooks `_ or try them online at `Binder `_. -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 `_. The basic recipe for this API works as follows: @@ -497,7 +497,7 @@ As a practical example, `this notebook