IRM and APO comparisons

doc/examples/index.rst

@@ -21,6 +21,7 @@ General Examples
     py_double_ml_pension.ipynb
     py_double_ml_sensitivity.ipynb
     py_double_ml_apo.ipynb
+    py_double_ml_irm_vs_apo.ipynb
     py_double_ml_learner.ipynb
     py_double_ml_firststage.ipynb
     py_double_ml_multiway_cluster.ipynb

doc/examples/py_double_ml_cate.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)."
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)."
    ]
   },
   {
@@ -409,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.12.3"
   },
   "vscode": {
    "interpreter": {

doc/examples/py_double_ml_cate_plr.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)."
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)."
    ]
   },
   {

doc/examples/py_double_ml_gate.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
     "\n",
     "$$\n",
     "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n",

doc/examples/py_double_ml_gate_plr.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
     "\n",
     "$$\n",
     "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n",