{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic Effects with `base_period = \"universal\"`\n",
    "\n",
    "This notebook shows how to estimate **dynamic** (event-study) treatment effects with the `csdid` package using the `base_period = \"universal\"` option of `ATTgt.fit()`.\n",
    "\n",
    "The `base_period` argument controls which pre-treatment period is used as the reference for each $ATT(g,t)$:\n",
    "\n",
    "* `base_period = \"varying\"` (default) — the base period **changes** with $t$. For pre-treatment periods, the base is $t-1$ (so pre-treatment $ATT(g,t)$ resemble a *placebo* test in differences). For post-treatment periods, the base is the last period before $g$ becomes treated.\n",
    "* `base_period = \"universal\"` — the base period is **fixed** to the last pre-treatment period for every group (with anticipation accounted for). The $ATT$ at the base period itself is normalized to zero. This matches the standard event-study normalization that fixes the *level* at $e = -1$.\n",
    "\n",
    "Both options give the same $ATT(g,t)$ for **post**-treatment periods. They differ only in how pre-treatment $ATT(g,t)$ are reported, which in turn changes the look of the event-study plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/d2cml-ai/csdid/\n",
    "# pip install git+https://github.com/d2cml-ai/DRDID\n",
    "from csdid.att_gt import ATTgt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We use the same `mpdta` dataset (county-level teen employment, 2003-2007) used elsewhere in this site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>countyreal</th>\n",
       "      <th>lpop</th>\n",
       "      <th>lemp</th>\n",
       "      <th>first.treat</th>\n",
       "      <th>treat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>8001</td>\n",
       "      <td>5.896761</td>\n",
       "      <td>8.461469</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>8001</td>\n",
       "      <td>5.896761</td>\n",
       "      <td>8.336870</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005</td>\n",
       "      <td>8001</td>\n",
       "      <td>5.896761</td>\n",
       "      <td>8.340217</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>8001</td>\n",
       "      <td>5.896761</td>\n",
       "      <td>8.378161</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007</td>\n",
       "      <td>8001</td>\n",
       "      <td>5.896761</td>\n",
       "      <td>8.487352</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  countyreal      lpop      lemp  first.treat  treat\n",
       "0  2003        8001  5.896761  8.461469         2007      1\n",
       "1  2004        8001  5.896761  8.336870         2007      1\n",
       "2  2005        8001  5.896761  8.340217         2007      1\n",
       "3  2006        8001  5.896761  8.378161         2007      1\n",
       "4  2007        8001  5.896761  8.487352         2007      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpdta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/mpdta.csv\")\n",
    "mpdta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying base period (default)\n",
    "\n",
    "First, the default. `base_period` does not need to be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Time</th>\n",
       "      <th>ATT(g, t)</th>\n",
       "      <th>Post</th>\n",
       "      <th>Std. Error</th>\n",
       "      <th>[95% Pointwise</th>\n",
       "      <th>Conf. Band]</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-0.0105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>-0.0727</td>\n",
       "      <td>0.0517</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-0.0704</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>-0.1565</td>\n",
       "      <td>0.0157</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2006</td>\n",
       "      <td>-0.1373</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>-0.2391</td>\n",
       "      <td>-0.0355</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.1008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>-0.1967</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>-0.0594</td>\n",
       "      <td>0.0724</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>2005</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>-0.0545</td>\n",
       "      <td>0.0490</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "      <td>-0.0046</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>-0.0507</td>\n",
       "      <td>0.0415</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.0412</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>-0.0938</td>\n",
       "      <td>0.0113</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>-0.0105</td>\n",
       "      <td>0.0716</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2007</td>\n",
       "      <td>2005</td>\n",
       "      <td>-0.0027</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>-0.0467</td>\n",
       "      <td>0.0412</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>2006</td>\n",
       "      <td>-0.0311</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>-0.0799</td>\n",
       "      <td>0.0177</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.0261</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>-0.0726</td>\n",
       "      <td>0.0205</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group  Time  ATT(g, t)  Post  Std. Error  [95% Pointwise  Conf. Band]   \n",
       "0    2004  2004    -0.0105     1      0.0232         -0.0727       0.0517   \n",
       "1    2004  2005    -0.0704     1      0.0321         -0.1565       0.0157   \n",
       "2    2004  2006    -0.1373     1      0.0379         -0.2391      -0.0355  *\n",
       "3    2004  2007    -0.1008     1      0.0357         -0.1967      -0.0049  *\n",
       "4    2006  2004     0.0065     0      0.0245         -0.0594       0.0724   \n",
       "5    2006  2005    -0.0028     0      0.0193         -0.0545       0.0490   \n",
       "6    2006  2006    -0.0046     1      0.0172         -0.0507       0.0415   \n",
       "7    2006  2007    -0.0412     1      0.0196         -0.0938       0.0113   \n",
       "8    2007  2004     0.0305     0      0.0153         -0.0105       0.0716   \n",
       "9    2007  2005    -0.0027     0      0.0164         -0.0467       0.0412   \n",
       "10   2007  2006    -0.0311     0      0.0182         -0.0799       0.0177   \n",
       "11   2007  2007    -0.0261     1      0.0173         -0.0726       0.0205   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_varying = ATTgt(\n",
    "    yname=\"lemp\",\n",
    "    gname=\"first.treat\",\n",
    "    idname=\"countyreal\",\n",
    "    tname=\"year\",\n",
    "    xformla=\"lemp~1\",\n",
    "    data=mpdta,\n",
    ").fit(est_method=\"dr\", base_period=\"varying\")\n",
    "\n",
    "mw_varying.summ_attgt().summary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Overall summary of ATT's based on event-study/dynamic aggregation:\n",
      "    ATT Std. Error  [95.0%  Conf. Int.]  \n",
      "-0.0772     0.0221 -0.1205       -0.034 *\n",
      "\n",
      "\n",
      "Dynamic Effects:\n",
      "   Event time  Estimate  Std. Error  [95.0% Simult.   Conf. Band   \n",
      "0          -3    0.0305      0.0155           0.0002      0.0608  *\n",
      "1          -2   -0.0006      0.0130          -0.0261      0.0250   \n",
      "2          -1   -0.0245      0.0142          -0.0522      0.0033   \n",
      "3           0   -0.0199      0.0132          -0.0458      0.0060   \n",
      "4           1   -0.0510      0.0173          -0.0848     -0.0171  *\n",
      "5           2   -0.1373      0.0371          -0.2100     -0.0646  *\n",
      "6           3   -0.1008      0.0327          -0.1650     -0.0366  *\n",
      "---\n",
      "Signif. codes: `*' confidence band does not cover 0\n",
      "Control Group:  Never Treated , \n",
      "Anticipation Periods:  0\n",
      "Estimation Method:  Doubly Robust\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mw_varying.aggte(typec=\"dynamic\")\n",
    "mw_varying.plot_aggte();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal base period\n",
    "\n",
    "Now set `base_period = \"universal\"` in the call to `fit`. Everything else stays the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No units in group 2004 in time period 1, e2\n",
      "No available control units for group 2004 in time period 1, e4\n",
      "No units in group 2006 in time period 3, e2\n",
      "No available control units for group 2006 in time period 3, e4\n",
      "No units in group 2007 in time period 4, e2\n",
      "No available control units for group 2007 in time period 4, e4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Time</th>\n",
       "      <th>ATT(g, t)</th>\n",
       "      <th>Post</th>\n",
       "      <th>Std. Error</th>\n",
       "      <th>[95% Pointwise</th>\n",
       "      <th>Conf. Band]</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-0.0105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0248</td>\n",
       "      <td>-0.0771</td>\n",
       "      <td>0.0561</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-0.0704</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>-0.1599</td>\n",
       "      <td>0.0191</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2006</td>\n",
       "      <td>-0.1373</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>-0.2406</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.1008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>-0.1882</td>\n",
       "      <td>-0.0135</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>2003</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>-0.0895</td>\n",
       "      <td>0.0820</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>-0.0490</td>\n",
       "      <td>0.0545</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "      <td>-0.0046</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>-0.0552</td>\n",
       "      <td>0.0460</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.0412</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>-0.0952</td>\n",
       "      <td>0.0127</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>-0.0678</td>\n",
       "      <td>0.0744</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2007</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>-0.0243</td>\n",
       "      <td>0.0919</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2007</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>-0.0176</td>\n",
       "      <td>0.0798</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>-0.0261</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>-0.0706</td>\n",
       "      <td>0.0184</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group  Time  ATT(g, t)  Post  Std. Error  [95% Pointwise  Conf. Band]   \n",
       "0    2004  2003        NaN     0         NaN             NaN          NaN   \n",
       "1    2004  2004    -0.0105     1      0.0248         -0.0771       0.0561   \n",
       "2    2004  2005    -0.0704     1      0.0334         -0.1599       0.0191   \n",
       "3    2004  2006    -0.1373     1      0.0385         -0.2406      -0.0339  *\n",
       "4    2004  2007    -0.1008     1      0.0326         -0.1882      -0.0135  *\n",
       "5    2006  2003    -0.0038     0      0.0320         -0.0895       0.0820   \n",
       "6    2006  2004     0.0028     0      0.0193         -0.0490       0.0545   \n",
       "7    2006  2005        NaN     0         NaN             NaN          NaN   \n",
       "8    2006  2006    -0.0046     1      0.0189         -0.0552       0.0460   \n",
       "9    2006  2007    -0.0412     1      0.0201         -0.0952       0.0127   \n",
       "10   2007  2003     0.0033     0      0.0265         -0.0678       0.0744   \n",
       "11   2007  2004     0.0338     0      0.0217         -0.0243       0.0919   \n",
       "12   2007  2005     0.0311     0      0.0182         -0.0176       0.0798   \n",
       "13   2007  2006        NaN     0         NaN             NaN          NaN   \n",
       "14   2007  2007    -0.0261     1      0.0166         -0.0706       0.0184   "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_universal = ATTgt(\n",
    "    yname=\"lemp\",\n",
    "    gname=\"first.treat\",\n",
    "    idname=\"countyreal\",\n",
    "    tname=\"year\",\n",
    "    xformla=\"lemp~1\",\n",
    "    data=mpdta,\n",
    ").fit(est_method=\"dr\", base_period=\"universal\")\n",
    "\n",
    "mw_universal.summ_attgt().summary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Overall summary of ATT's based on event-study/dynamic aggregation:\n",
      "    ATT Std. Error  [95.0%  Conf. Int.]  \n",
      "-0.0772     0.0209 -0.1182      -0.0363 *\n",
      "\n",
      "\n",
      "Dynamic Effects:\n",
      "   Event time  Estimate  Std. Error  [95.0% Simult.   Conf. Band   \n",
      "0          -4    0.0033      0.0233          -0.0424      0.0490   \n",
      "1          -3    0.0250      0.0183          -0.0108      0.0608   \n",
      "2          -2    0.0245      0.0139          -0.0028      0.0517   \n",
      "3           0   -0.0199      0.0119          -0.0433      0.0035   \n",
      "4           1   -0.0510      0.0182          -0.0865     -0.0154  *\n",
      "5           2   -0.1373      0.0397          -0.2150     -0.0595  *\n",
      "6           3   -0.1008      0.0326          -0.1646     -0.0370  *\n",
      "---\n",
      "Signif. codes: `*' confidence band does not cover 0\n",
      "Control Group:  Never Treated , \n",
      "Anticipation Periods:  0\n",
      "Estimation Method:  Doubly Robust\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mw_universal.aggte(typec=\"dynamic\")\n",
    "mw_universal.plot_aggte();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}