Merge pull request #246 from DoubleML/dev

Add Citation & Maintenance

Author: SvenKlaassen

doc/examples/py_double_ml_meets_flaml.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "This notebook will use [FLAML](https://github.com/microsoft/FLAML), but there are also many other AutoML frameworks. Particularly useful for DoubleML are packages that provide some way to export the model in `sklearn`-style.\n",
     "\n",
-    "Examples are: [TPOT](https://epistasislab.github.io/tpot/), [autosklearn](https://automl.github.io/auto-sklearn/master/), [H20](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) or [Gama](https://openml-labs.github.io/gama/master/)."
+    "Examples are: [TPOT](https://epistasislab.github.io/tpot/), [autosklearn](https://automl.github.io/auto-sklearn/master/), [H20](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) or [Gama](https://github.com/amore-labs/gama)."
    ]
   },
   {

doc/examples/py_double_ml_robust_iv.ipynb

@@ -10,6 +10,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6c070d4e",
    "metadata": {},
    "source": [
     "In this example we will show how to use the DoubleML package to obtain confidence sets for the treatment effects that are robust to weak instruments. Weak instruments are those that have a relatively weak correlation with the treatment. It is well known that in this case, standard methods to construct confidence intervals have poor properties and can have coverage much lower than the nominal value. We will assume that the reader of this notebook is already familiar with DoubleML and how it can be used to fit instrumental variable models.\n",
@@ -29,7 +30,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "f9e7a46f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,6 +48,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b6b976ce",
    "metadata": {},
    "source": [
     "# Running a small simulation"
@@ -61,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "82111204",
    "metadata": {},
    "outputs": [],
@@ -86,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "9a347c25",
    "metadata": {},
    "outputs": [],
@@ -117,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "600b8196",
    "metadata": {},
    "outputs": [],
@@ -163,26 +166,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "86c83edc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                     DML coverage  Robust coverage  DML median length  \\\n",
-      "instrument_strength                                                     \n",
-      "0.003                        0.15             0.91           0.489567   \n",
-      "1.000                        0.93             0.92           0.572717   \n",
-      "\n",
-      "                     Robust median length  \n",
-      "instrument_strength                        \n",
-      "0.003                                 inf  \n",
-      "1.000                            0.582754  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results_df = pd.DataFrame(output_list)\n",
     "summary_df = results_df.groupby(\"instrument_strength\").agg(\n",
@@ -211,6 +198,7 @@
     "- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., and Hansen, C. (2018). Double/debiased machine learning for\n",
     "treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68.\n",
     "- Ma, Y. (2023). Identification-robust inference for the late with high-dimensional covariates. arXiv preprint arXiv:2302.09756.\n",
+    "- Smucler, E., Lanni, L., Masip, D. (2025). A note on the properties of the confidence set for the local average treatment effect obtained by inverting the score test. arXiv preprint 2506.10449\n",
     "- Stock, J. H. and Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5):1055–1096.\n",
     "- Takatsu, K., Levis, A. W., Kennedy, E., Kelz, R., and Keele, L. (2023). Doubly robust machine learning for an instrumental\n",
     "variable study of surgical care for cholecystitis. arXiv preprint arXiv:2307.06269."
@@ -233,7 +221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,

doc/index.rst

@@ -200,20 +200,38 @@ and `https://github.com/DoubleML/doubleml-for-r/issues <https://github.com/Doubl
 Citation
 --------
 
-If you use the DoubleML package a citation is highly appreciated:
+If you use the DoubleML package a citation is highly appreciated. To cite the Python package, please use the following reference:
+
+Bach, P., Chernozhukov, V., Klaassen, S., Kurz, M. S., & Spindler, M. DoubleML - Double
+Machine Learning in Python [Computer software]. `https://github.com/DoubleML/doubleml-for-py <https://github.com/DoubleML/doubleml-for-py>`_.
+
+An accompanying paper to the DoubleML package for Python has been published in the Journal of Machine Learning Research. For citation, please use the following reference:
 
 Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An
 Object-Oriented Implementation of Double Machine Learning in Python,
 Journal of Machine Learning Research, 23(53): 1-6,
 `https://www.jmlr.org/papers/v23/21-0862.html  <https://www.jmlr.org/papers/v23/21-0862.html>`_.
 
 
+For the R package, please use the following reference:
+
 Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M.  and Klaassen, S. (2024),
 DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, Journal of Statistical Software,
 108(3): 1-56, doi:`10.18637/jss.v108.i03 <https://doi.org/10.18637/jss.v108.i03>`_, arXiv:`2103.09603 <https://arxiv.org/abs/2103.09603>`_.
 
 Bibtex-entries:
 
+.. code-block:: TeX
+
+    @software{DoubleML,
+      title    = {{DoubleML} -- Double Machine Learning in Python},
+      author   = {Bach, Philipp and Chernozhukov, Victor and Klaassen, Sven and Kurz, Malte S. and Spindler, Martin},
+      year     = {2024},
+      version  = {latest},
+      url      = {https://github.com/DoubleML/doubleml-for-py},
+      note     = {BSD-3-Clause License. Documentation: \url{https://docs.doubleml.org/stable/index.html}}
+    }
+
 .. code-block:: TeX
 
     @article{DoubleML2022Python,
@@ -250,6 +268,9 @@ Foundation) is acknowledged – Project Number 431701914.
 References
 ----------
 
+Bach, P., Chernozhukov, V., Klaassen, S., Kurz, M. S., & Spindler, M. DoubleML - Double
+Machine Learning in Python [Computer software]. `https://github.com/DoubleML/doubleml-for-py <https://github.com/DoubleML/doubleml-for-py>`_.
+
 Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An
 Object-Oriented Implementation of Double Machine Learning in Python,
 Journal of Machine Learning Research, 23(53): 1-6,