Update Documentation for 0.10.1

doc/examples/did/py_panel.ipynb

@@ -64,7 +64,7 @@
    "source": [
     "### Data Details\n",
     "\n",
-    "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n",
+    "Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).\n",
     "\n",
     "More specifically\n",
     "\n",
@@ -873,7 +873,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### All combinations\n",
+    "### All Combinations\n",
     "\n",
     "The  option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n",
     "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)."
@@ -891,6 +891,29 @@
     "dml_obj_all.plot_effects()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Universal Base Period\n",
+    "\n",
+    "The  option `gt_combinations=\"universal\"` set $t_\\text{pre} = \\mathrm{g} - \\delta - 1$, corresponding to a universal/constant comparison or base period.\n",
+    "\n",
+    "Remark that this implies $t_\\text{pre} > t_\\text{eval}$ for all pre-treatment periods (accounting for anticipation). Therefore these effects do not have the same straightforward interpretation as ATT's."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dml_obj_universal = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"universal\"}))\n",
+    "dml_obj_universal.fit()\n",
+    "dml_obj_universal.bootstrap(n_rep_boot=5000)\n",
+    "dml_obj_universal.plot_effects()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},