update literature

Author: SvenKlaassen

doc/examples/py_double_ml_cate.ipynb

@@ -12,14 +12,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
    "source": [
     "## Data\n",
     "\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 this [paper](https://arxiv.org/abs/1806.03467) and this implementation from [EconML](https://github.com/microsoft/EconML)."
+    "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) and this [notebook](https://github.com/py-why/EconML/blob/main/notebooks/Causal%20Forest%20and%20Orthogonal%20Random%20Forest%20Examples.ipynb) from [EconML](https://github.com/py-why/EconML)."
    ]
   },
   {
@@ -80,33 +81,6 @@
     "    return te\n",
     "\n",
     "def create_synthetic_data(n_samples=200, n_w=30, support_size=5, n_x=1):\n",
-    "    \"\"\"\n",
-    "    Creates a simple synthetic example for conditional treatment effects.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    n_samples : int\n",
-    "        Number of samples.\n",
-    "        Default is ``200``.\n",
-    "\n",
-    "    n_w : int\n",
-    "        Dimension of covariates.\n",
-    "        Default is ``30``.\n",
-    "\n",
-    "    support_size : int\n",
-    "        Number of relevant covariates.\n",
-    "        Default is ``5``.\n",
-    "\n",
-    "    n_x : int\n",
-    "        Dimension of treatment variable.\n",
-    "        Default is ``1``.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "     data : pd.DataFrame\n",
-    "            A data frame.\n",
-    "\n",
-    "    \"\"\"\n",
     "    # Outcome support\n",
     "    # With the next two lines we are effectively choosing the matrix gamma in the example\n",
     "    support_y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
@@ -219,12 +193,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
    "source": [
-    "To estimate the CATE, we rely on the best-linear-predictor of the linear score as in [Semenova et al.](https://doi.org/10.1093/ectj/utaa027) To approximate the target function $g(x)$ with a linear form, we have to define a data frame of basis functions. Here, we rely on [patsy](https://patsy.readthedocs.io/en/latest/) to construct a suitable basis of [B-splines](https://en.wikipedia.org/wiki/B-spline)."
+    "To estimate the CATE, we rely on the best-linear-predictor of the linear score as in [Semenova et al. (2021)](https://doi.org/10.1093/ectj/utaa027) To approximate the target function $g(x)$ with a linear form, we have to define a data frame of basis functions. Here, we rely on [patsy](https://patsy.readthedocs.io/en/latest/) to construct a suitable basis of [B-splines](https://en.wikipedia.org/wiki/B-spline)."
    ]
   },
   {

doc/literature/literature.rst

@@ -103,6 +103,12 @@ Double machine learning literature
           :link-badge:`https://arxiv.org/abs/1909.03489,"arXiv",cls=badge-dark`
           |hr|
 
+        - Nathan Kallus, Xiaojie Mao, Masatoshi Uehara |br|
+          **Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond** |br|
+          *arXiv preprint arXiv:1912.12945 [stat.ML], 2019* |br|
+          :opticon:`link` :link-badge:`https://doi.org/10.48550/arXiv.1912.12945,"arXiv",cls=badge-dark`
+          |hr|
+
         - Nathan Kallus, Masatoshi Uehara |br|
           **Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes** |br|
           *Journal of Machine Learning Research 21, 1-63, 2020* |br|
@@ -123,6 +129,11 @@ Double machine learning literature
           :link-badge:`https://arxiv.org/abs/1711.00342,"arXiv",cls=badge-dark`
           |hr|
 
+        - Vira Semenova, Victor Chernozhukov |br|
+          **Debiased machine learning of conditional average treatment effects and other causal functions** |br|
+          *The Econometrics Journal, 2021* |br|
+          :opticon:`link` :link-badge:`https://doi.org/10.1093/ectj/utaa027,"URL",cls=badge-dark`
+
         - Vira Semenova, Matt Goldman, Victor Chernozhukov, Matt Taddy |br|
           **Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels** |br|
           *arXiv preprint arXiv:1712.09988 [stat.ML], 2017* |br|