Merge pull request #218 from DoubleML/dev

Documentation Maintenance

Author: SvenKlaassen

Diff for: doc/conf.py

@@ -14,9 +14,13 @@
 import sys
 
 import doubleml
+import plotly.io as pio
 
 sys.path.insert(0, os.path.abspath('..'))
 
+# Set the default renderer for Plotly
+pio.renderers.default = 'sphinx_gallery'
+
 # -- Project information -----------------------------------------------------
 
 project = 'DoubleML'

Diff for: doc/examples/py_double_ml_firststage.ipynb

@@ -205,6 +205,8 @@
     "\n",
     "## References\n",
     "\n",
+    "Bach, P., Schacht, O., Chernozhukov, V., Klaassen, S., & Spindler, M. (2024, March). Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study. In Causal Learning and Reasoning (pp. 1065-1117). PMLR.\n",
+    "\n",
     "Alexandre Belloni, Victor Chernozhukov, and Christian Hansen. Inference on Treatment Effects after Selection among High-Dimensional Controls. *The Review of Economic Studies*, 81(2):608–650, 11 2013. ISSN 0034-6527. doi: 10.1093/restud/rdt044. URL https://doi.org/10.1093/restud/rdt044.\n",
     "\n",
     "\n",

Diff for: doc/examples/py_double_ml_learner.ipynb

@@ -530,6 +530,15 @@
     "print(f'Time with LightGBM: {round(t_3_stop - t_3_start, 4)} seconds')\n",
     "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_3_stop - t_3_start), 2)}')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "Bach, P., Schacht, O., Chernozhukov, V., Klaassen, S., & Spindler, M. (2024, March). Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study. In Causal Learning and Reasoning (pp. 1065-1117). PMLR."
+   ]
   }
  ],
  "metadata": {

Diff for: doc/examples/py_double_ml_meets_flaml.ipynb

@@ -916,7 +916,11 @@
     "\n",
     "This notebook highlights that tuning plays an important role and can be easily done using FLAML AutoML. In our [recent study](https://arxiv.org/abs/2402.04674) we provide more evidence for tuning with AutoML, especially that the full sample case in all investigated cases performed similarly to the full sample case and thus tuning time and complexity can be saved by tuning externally.\n",
     "\n",
-    "See also our fully automated API for tuning DoubleML objects using AutoML, called [AutoDoubleML](https://github.com/OliverSchacht/AutoDoubleML) which can be installed from Github for python."
+    "See also our fully automated API for tuning DoubleML objects using AutoML, called [AutoDoubleML](https://github.com/OliverSchacht/AutoDoubleML) which can be installed from Github for python.\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "Bach, P., Schacht, O., Chernozhukov, V., Klaassen, S., & Spindler, M. (2024, March). Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study. In Causal Learning and Reasoning (pp. 1065-1117). PMLR."
    ]
   }
  ],

Diff for: doc/index.rst

@@ -142,7 +142,7 @@ Double Machine Learning Algorithm
 .. raw:: html
 
     <p align="center">
-        <iframe width="500" height="400" src="https://www.youtube.com/embed/BMAr27rp4uA" title="Cross-Fitting Animation" frameborder="0" allowfullscreen></iframe>
+        <iframe width="500" height="400" src="https://www.youtube.com/embed/NWEoLe8mZqM?si=qJxpUOJb2ROqNogY" title="Cross-Fitting Animation" frameborder="0" allowfullscreen></iframe>
     </p>
 
 

Diff for: requirements.txt

@@ -9,14 +9,14 @@ sphinx==8.1.3
 sphinx-copybutton
 nbsphinx==0.9.6
 ipykernel
-sphinx-gallery
+sphinx-gallery==0.18.0
 sphinx-panels
 sphinx-design
 jupyter-sphinx
 pydata-sphinx-theme==0.15.4
 pickleshare
 matplotlib
-plotly
+plotly==5.24.1
 seaborn
 xgboost
 lightgbm