Update documentation location

kbattocchi · kbattocchi · commit f54fa02149ae · 2025-07-24T13:36:16.000-04:00
Signed-off-by: Keith Battocchi &lt;kebatt@microsoft.com&gt;
diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@
 [![Supported Python versions](https://img.shields.io/pypi/pyversions/econml.svg)](https://pypi.org/project/econml/)
 
 <h1>
-<a href="https://econml.azurewebsites.net/">
+<a href="https://www.pywhy.org/EconML/">
 <img src="doc/econml-logo-icon.png" width="80px" align="left" style="margin-right: 10px;", alt="econml-logo"> 
 </a> EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
 </h1>
@@ -20,7 +20,7 @@ techniques with econometrics to bring automation to complex causal inference pro
 One of the biggest promises of machine learning is to automate decision making in a multitude of domains. At the core of many data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the causal effect of an intervention on an outcome of interest for a sample with a particular set of features? In a nutshell, this toolkit is designed to measure the causal effect of some treatment variable(s) `T` on an outcome 
 variable `Y`, controlling for a set of features `X, W` and how does that effect vary as a function of `X`. The methods implemented are applicable even with observational (non-experimental or historical) datasets. For the estimation results to have a causal interpretation, some methods assume no unobserved confounders (i.e. there is no unobserved variable not included in `X, W` that simultaneously has an effect on both `T` and `Y`), while others assume access to an instrument `Z` (i.e. an observed variable `Z` that has an effect on the treatment `T` but no direct effect on the outcome `Y`). Most methods provide confidence intervals and inference results.
 
-For detailed information about the package, consult the documentation at https://econml.azurewebsites.net/.
+For detailed information about the package, consult the documentation at https://www.pywhy.org/EconML/.
 
 For information on use cases and background material on causal inference and heterogeneous treatment effects see our webpage at https://www.microsoft.com/en-us/research/project/econml/
 
@@ -626,7 +626,7 @@ importances = policy.feature_importances_
   ![image](images/policy_tree.png)
 </details>
 
-To see more complex examples, go to the [notebooks](https://github.com/py-why/EconML/tree/main/notebooks) section of the repository. For a more detailed description of the treatment effect estimation algorithms, see the EconML [documentation](https://econml.azurewebsites.net/).
+To see more complex examples, go to the [notebooks](https://github.com/py-why/EconML/tree/main/notebooks) section of the repository. For a more detailed description of the treatment effect estimation algorithms, see the EconML [documentation](https://www.pywhy.org/EconML/).
 
 # For Developers
 
diff --git a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "# Orthogonal Random Forest and Causal Forest: Use Cases and Examples\n",
     "\n",
-    "Causal Forests and Generalized Random Forests are a flexible method for estimating treatment effect heterogeneity with Random Forests. Orthogonal Random Forest (ORF) combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests. Due to the orthogonalization aspect of this method, the ORF performs especially well in the presence of high-dimensional confounders. For more details, see [this paper](https://arxiv.org/abs/1806.03467) or the [EconML docummentation](https://econml.azurewebsites.net/).\n",
+    "Causal Forests and Generalized Random Forests are a flexible method for estimating treatment effect heterogeneity with Random Forests. Orthogonal Random Forest (ORF) combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests. Due to the orthogonalization aspect of this method, the ORF performs especially well in the presence of high-dimensional confounders. For more details, see [this paper](https://arxiv.org/abs/1806.03467) or the [EconML docummentation](https://www.pywhy.org/EconML/).\n",
     "\n",
     "The EconML SDK implements the following OrthoForest variants:\n",
     "\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb
@@ -1194,7 +1194,7 @@
     "* Interpret the resulting individual-level treatment effects\n",
     "* Make the policy decision beats the previous and baseline policies\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). \n",
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). \n",
     "\n",
     "To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/).\n"
    ]
diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb
@@ -842,7 +842,7 @@
     "* Interpret the resulting individual-level treatment effects\n",
     "* Make the policy decision beats the previous and baseline policies\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "
    ]
   }
  ],
diff --git a/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment via Short-Term Proxies.ipynb b/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment via Short-Term Proxies.ipynb
@@ -793,7 +793,7 @@
     "* use Machine Learning to enable estimation with high-dimensional surrogates and controls\n",
     "* solve a complex problem using an unified pipeline with only a few lines of code\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "
    ]
   }
  ],
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb
@@ -1451,7 +1451,7 @@
     "* Build investment policies around the learned effects\n",
     "* Test causal assumptions and investigate the robustness of the estimates\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). \n",
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). \n",
     "\n",
     "To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/)."
    ]
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb
@@ -1222,7 +1222,7 @@
     "* Interpret the resulting individual-level treatment effects\n",
     "* Build investment policies around the learned effects\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "
    ]
   },
   {
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb
@@ -1281,7 +1281,7 @@
     "* Intepret individual-level treatment effects\n",
     "* Build policies around the learned effects\n",
     "\n",
-    "To learn more about what EconML can do for you, visit the [website](https://aka.ms/econml), [GitHub page](https://github.com/py-why/EconML) or [docummentation](https://econml.azurewebsites.net/).\n",
+    "To learn more about what EconML can do for you, visit the [website](https://aka.ms/econml), [GitHub page](https://github.com/py-why/EconML) or [docummentation](https://www.pywhy.org/EconML/).\n",
     "\n",
     "To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/)."
    ]
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb
@@ -747,7 +747,7 @@
     "* Intepret the resulting individual-level treatment effects\n",
     "* Build policies around the learned effects\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "
    ]
   }
  ],
diff --git a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb b/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb
@@ -1134,7 +1134,7 @@
     "* Substantially improve performance when reweighting samples with analytical confidence intervals.\n",
     "* Learn treatment effect heterogeneity and recover the same insight from using observational dataset.\n",
     "\n",
-    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
+    "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "
    ]
   }
  ],
diff --git a/notebooks/Dynamic Double Machine Learning Examples.ipynb b/notebooks/Dynamic Double Machine Learning Examples.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "# Dynamic Double Machine Learning: Use Cases and Examples\n",
     "\n",
-    "Dynamic DoubleML is an extension of the Double ML approach for treatments assigned sequentially over time periods. This estimator will account for treatments that can have causal effects on future outcomes. For more details, see [this paper](https://arxiv.org/abs/2002.07285) or the [EconML docummentation](https://econml.azurewebsites.net/).\n",
+    "Dynamic DoubleML is an extension of the Double ML approach for treatments assigned sequentially over time periods. This estimator will account for treatments that can have causal effects on future outcomes. For more details, see [this paper](https://arxiv.org/abs/2002.07285) or the [EconML docummentation](https://www.pywhy.org/EconML/).\n",
     "\n",
     "For example, the Dynamic DoubleML could be useful in estimating the following causal effects:\n",
     "* the effect of investments on revenue at companies that receive investments at regular intervals ([see more](https://arxiv.org/abs/2103.08390))\n",
diff --git a/notebooks/Treatment Featurization Examples.ipynb b/notebooks/Treatment Featurization Examples.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "Many EconML estimators model treatment effects as linear with respect to the treatment. However, there are times when one may want to impose a particular form of non-linearity in the treatment effect. For example, one may believe that the outcome is linear with respect to treatment squared, or more generally, linear with respect to some featurization of the treatment. We enable users to pass such a featurization via the `treatment_featurizer` argument. This notebook walks through some examples where treatment featurization may be useful. \n",
     "\n",
-    "Additional context and problem setup can be found in the docs at [this link](https://econml.azurewebsites.net/spec/api.html#linear-in-treatment-cate-estimators).\n",
+    "Additional context and problem setup can be found in the docs at [this link](https://www.pywhy.org/EconML/spec/api.html#linear-in-treatment-cate-estimators).\n",
     "\n",
     "The EconmL SDK supports treatment featurization for many estimators, including:\n",
     "\n",
diff --git a/pyproject.toml b/pyproject.toml
@@ -35,7 +35,7 @@ dependencies = [
 "Homepage" = "https://github.com/py-why/EconML"
 "Bug Tracker" = "https://github.com/py-why/EconML/Issues"
 "Source Code" = "https://github.com/py-why/EconML" 
-"Documentation" = "https://econml.azurewebsites.net/"
+"Documentation" = "https://www.pywhy.org/EconML/"
 
 [project.optional-dependencies]
 automl = [

Original file line number	Diff line number	Diff line change
`@@ -1194,7 +1194,7 @@`
`1194`	`1194`	`"* Interpret the resulting individual-level treatment effects\n",`
`1195`	`1195`	`"* Make the policy decision beats the previous and baseline policies\n",`
`1196`	`1196`	`"\n",`
`1197`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). \n",`
	`1197`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). \n",`
`1198`	`1198`	`"\n",`
`1199`	`1199`	`"To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/).\n"`
`1200`	`1200`	`]`
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`@@ -842,7 +842,7 @@`
`842`	`842`	`"* Interpret the resulting individual-level treatment effects\n",`
`843`	`843`	`"* Make the policy decision beats the previous and baseline policies\n",`
`844`	`844`	`"\n",`
`845`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "`
	`845`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "`
`846`	`846`	`]`
`847`	`847`	`}`
`848`	`848`	`],`
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`@@ -793,7 +793,7 @@`
`793`	`793`	`"* use Machine Learning to enable estimation with high-dimensional surrogates and controls\n",`
`794`	`794`	`"* solve a complex problem using an unified pipeline with only a few lines of code\n",`
`795`	`795`	`"\n",`
`796`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "`
	`796`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "`
`797`	`797`	`]`
`798`	`798`	`}`
`799`	`799`	`],`
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`@@ -1451,7 +1451,7 @@`
`1451`	`1451`	`"* Build investment policies around the learned effects\n",`
`1452`	`1452`	`"* Test causal assumptions and investigate the robustness of the estimates\n",`
`1453`	`1453`	`"\n",`
`1454`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). \n",`
	`1454`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). \n",`
`1455`	`1455`	`"\n",`
`1456`	`1456`	`"To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/)."`
`1457`	`1457`	`]`
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`@@ -1222,7 +1222,7 @@`
`1222`	`1222`	`"* Interpret the resulting individual-level treatment effects\n",`
`1223`	`1223`	`"* Build investment policies around the learned effects\n",`
`1224`	`1224`	`"\n",`
`1225`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "`
	`1225`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "`
`1226`	`1226`	`]`
`1227`	`1227`	`},`
`1228`	`1228`	`{`
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`@@ -1281,7 +1281,7 @@`
`1281`	`1281`	`"* Intepret individual-level treatment effects\n",`
`1282`	`1282`	`"* Build policies around the learned effects\n",`
`1283`	`1283`	`"\n",`
`1284`		`- "To learn more about what EconML can do for you, visit the [website](https://aka.ms/econml), [GitHub page](https://github.com/py-why/EconML) or [docummentation](https://econml.azurewebsites.net/).\n",`
	`1284`	`+ "To learn more about what EconML can do for you, visit the [website](https://aka.ms/econml), [GitHub page](https://github.com/py-why/EconML) or [docummentation](https://www.pywhy.org/EconML/).\n",`
`1285`	`1285`	`"\n",`
`1286`	`1286`	`"To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/py-why/dowhy) or [documentation](https://www.pywhy.org/dowhy/)."`
`1287`	`1287`	`]`
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`@@ -747,7 +747,7 @@`
`747`	`747`	`"* Intepret the resulting individual-level treatment effects\n",`
`748`	`748`	`"* Build policies around the learned effects\n",`
`749`	`749`	`"\n",`
`750`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "`
	`750`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "`
`751`	`751`	`]`
`752`	`752`	`}`
`753`	`753`	`],`
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`@@ -1134,7 +1134,7 @@`
`1134`	`1134`	`"* Substantially improve performance when reweighting samples with analytical confidence intervals.\n",`
`1135`	`1135`	`"* Learn treatment effect heterogeneity and recover the same insight from using observational dataset.\n",`
`1136`	`1136`	`"\n",`
`1137`		`- "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "`
	`1137`	`+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://www.pywhy.org/EconML/). "`
`1138`	`1138`	`]`
`1139`	`1139`	`}`
`1140`	`1140`	`],`