Automated updates to slides.

Author: Paul Schrimpf

lectures/paul/causality_intro.html

@@ -76,7 +76,7 @@
         "\n",
         "## Selection Bias\n",
         "\n",
-        "-   Comparing ATE and APE $$ %\n",
+        "-   Comparing ATE and average population effect $$ %\n",
         "    \\begin{align*}\n",
         "    ATE = & \\Er[Y_i(1) - Y_i(0)] \\\\\n",
         "    = & \\overbrace{\\Er[Y_i(1) - Y_i(0) | T_i=1]}^{\\text{avg treatment effect  on treated}} P(T_i=1) + \\overbrace{\\Er[Y_i(1) - Y_i(0) | T_i=0]}^{\\text{avg treatment effect on untreated}} P(T_i=0) \\\\\n",
@@ -109,7 +109,18 @@
         "    -   $\\epsilon_i(d) \\sim N(0,\\sigma^2)$, independent\n",
         "    -   Chooses $\\max_d \\Er[Y_i(d)|S_i(0), S_i(1)] = \\max_d S_i(d)$\n",
         "\n",
-        "``` python\n",
+        "## Selection Bias Example"
+      ],
+      "id": "27d91c9d-ce50-4c4e-975d-c2b064446aa8"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "output-location": "slide"
+      },
+      "outputs": [],
+      "source": [
         "import numpy as np\n",
         "np.random.seed(0)\n",
         "class selectiondata:\n",
@@ -144,14 +155,14 @@
         "print(\"|APE|ATE|Selection Bias|\\n\" +\n",
         "      \"|---|---|---|\\n\" +\n",
         "      f\"|{data.APE():.2}|{data.ATE():.2}|{data.selectionbias():.2}|\\n\"\n",
-        "      f\"|σ={s:.2}|\\n\\n\")\n",
-        "```\n",
-        "\n",
-        "| APE   | ATE  | Selection Bias |\n",
-        "|-------|------|----------------|\n",
-        "| 0.27  | 0.53 | -0.26          |\n",
-        "| σ=0.5 |      |                |\n",
-        "\n",
+        "      f\"|σ={s:.2}|\\n\\n\")"
+      ],
+      "id": "1168a686"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "# Random Experiments\n",
         "\n",
         "## Random Experiment\n",
@@ -183,12 +194,14 @@
         "\n",
         "## Example Pfizer Covid Vaccine RCT"
       ],
-      "id": "8cf0bac1-836d-44a0-9df0-7c4bccf9940e"
+      "id": "99c95cd9-b1fd-42d4-8141-377621fdb856"
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
+      "execution_count": 2,
+      "metadata": {
+        "output-location": "slide"
+      },
       "outputs": [
         {
           "output_type": "stream",
@@ -244,21 +257,22 @@
         "\n",
         "print(\"\\n- All\\n\\n\" + pfizerall.table() + \"\\n - 65+\\n\\n\" + pfizer65.table())"
       ],
-      "id": "09523551"
+      "id": "b29841c4"
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "## Sources and Further Reading\n",
         "\n",
-        "-   [Chapter\n",
-        "    2](https://causalml-book.org/assets/chapters/CausalML_chap_2.pdf) of\n",
-        "    Chernozhukov et al. (2024) is the basis for much of these slides,\n",
+        "-   [Chapter 2](http://chapters.causalml-book.org/CausalML_chap_2.pdf)\n",
+        "    of Chernozhukov et al. (2024) is the basis for much of these slides,\n",
         "    inlcuding the Pfizer/BioNTech Covid Vaccine RCT example\n",
         "-   [Chapter\n",
         "    1](https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html)\n",
         "    of Facure (2022)\n",
+        "-   Heckman and Pinto (2024) for a critique of potential outcomes and\n",
+        "    argument to use more structured economic models\n",
         "\n",
         "## References\n",
         "\n",
@@ -267,9 +281,13 @@
         "<https://causalml-book.org/>.\n",
         "\n",
         "Facure, Matheus. 2022. *Causal Inference for the Brave and True*.\n",
-        "<https://matheusfacure.github.io/python-causality-handbook/landing-page.html>."
+        "<https://matheusfacure.github.io/python-causality-handbook/landing-page.html>.\n",
+        "\n",
+        "Heckman, James, and Rodrigo Pinto. 2024. “Econometric Causality: The\n",
+        "Central Role of Thought Experiments.” *Journal of Econometrics* 243 (1):\n",
+        "105719. https://doi.org/<https://doi.org/10.1016/j.jeconom.2024.105719>."
       ],
-      "id": "d369e916-c521-4e77-8261-95dd7d766074"
+      "id": "52171e20-38bd-4c0e-9b8b-6cecc19dcbd0"
     }
   ],
   "nbformat": 4,