Fit_OLS_Model_data102final.ipynb

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    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haroldcha/gunviolencedata/blob/master/causal_inference.ipynb)\n"
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    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import datetime\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
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   "cell_type": "markdown",
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    "Using difference-in-difference, we can see if our selected intervention, the 2014 Act 192, caused an increase in injuries + fatalities per incident. Our hypothesis is that if gun advocates can sue Philadelphia municipalities for gun legislation, more violent guns will be available and there will be a higher injury + death rate per gun incident.\n",
    "\n",
    "Our value of interest $y$ is the number of deaths in a given incident. Our treatment value $T$ is a binary value indicating 1 if the incident occurred in South Philadelphia, PA or 0 if in Camden, NJ. We also have a value $I$ to indicate 1 if the violent incident occurred after the intervention, and 0 if it occurred before.\n",
    "\n",
    "Since South Philadelphia and Camden are adjacent, we can exclude confounders such as weather, geography, etc. We'll be controlling for population density, median household income, unemployment rates, and the median age."
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   "source": [
    "phil_camden = pd.read_csv('https://raw.githubusercontent.com/haroldcha/gunviolencedata/master/philadelphia_camden_gun_violence.csv')"
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       "     Unnamed: 0  incident_id        date         state city_or_county  \\\n",
       "0           288        92423  2014-01-01  Pennsylvania   Philadelphia   \n",
       "1           297        92747  2014-01-01    New Jersey         Camden   \n",
       "2           532        92349  2014-01-02  Pennsylvania   Philadelphia   \n",
       "3           557       220357  2014-01-02  Pennsylvania   Philadelphia   \n",
       "4           604        92727  2014-01-02    New Jersey         Camden   \n",
       "..          ...          ...         ...           ...            ...   \n",
       "604      235382      1062115  2018-03-04  Pennsylvania   Philadelphia   \n",
       "605      235559      1063484  2018-03-05    New Jersey         Camden   \n",
       "606      236316      1066841  2018-03-10  Pennsylvania   Philadelphia   \n",
       "607      237129      1070055  2018-03-15  Pennsylvania   Philadelphia   \n",
       "608      239385      1080442  2018-03-29    New Jersey         Camden   \n",
       "\n",
       "                              address  n_killed  n_injured  \\\n",
       "0    34th Street and Haverford Avenue         0          1   \n",
       "1                                 NaN         0          1   \n",
       "2                          7th Street         0          1   \n",
       "3           34th and Haverford Avenue         0          1   \n",
       "4          1600 block of Louis Street         1          0   \n",
       "..                                ...       ...        ...   \n",
       "604     N 6th St and Spring Garden St         1          1   \n",
       "605          400 block of Mechanic St         1          0   \n",
       "606           200 block of Lombard St         0          1   \n",
       "607      700 block of S Columbus Blvd         0          0   \n",
       "608         Kaighn Ave and Haddon Ave         0          4   \n",
       "\n",
       "                                          incident_url  \\\n",
       "0     http://www.gunviolencearchive.org/incident/92423   \n",
       "1     http://www.gunviolencearchive.org/incident/92747   \n",
       "2     http://www.gunviolencearchive.org/incident/92349   \n",
       "3    http://www.gunviolencearchive.org/incident/220357   \n",
       "4     http://www.gunviolencearchive.org/incident/92727   \n",
       "..                                                 ...   \n",
       "604  http://www.gunviolencearchive.org/incident/106...   \n",
       "605  http://www.gunviolencearchive.org/incident/106...   \n",
       "606  http://www.gunviolencearchive.org/incident/106...   \n",
       "607  http://www.gunviolencearchive.org/incident/107...   \n",
       "608  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                            source_url  ...  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...  ...   \n",
       "1    http://www.courierpostonline.com/article/20140...  ...   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...  ...   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...  ...   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...  ...   \n",
       "..                                                 ...  ...   \n",
       "604  http://6abc.com/1-killed-1-injured-in-spring-g...  ...   \n",
       "605  http://www.nj.com/camden/index.ssf/2018/03/man...  ...   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...  ...   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...  ...   \n",
       "608  https://www.courierpostonline.com/story/news/c...  ...   \n",
       "\n",
       "                               participant_type  \\\n",
       "0                 0::Victim||1::Subject-Suspect   \n",
       "1                 0::Victim||1::Subject-Suspect   \n",
       "2      0::Victim||1::Victim||2::Subject-Suspect   \n",
       "3                 0::Victim||1::Subject-Suspect   \n",
       "4                                     0::Victim   \n",
       "..                                          ...   \n",
       "604                        0::Victim||1::Victim   \n",
       "605                                   0::Victim   \n",
       "606                                   0::Victim   \n",
       "607      0::Subject-Suspect||1::Subject-Suspect   \n",
       "608  0::Victim||1::Victim||2::Victim||3::Victim   \n",
       "\n",
       "                                               sources state_house_district  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...                  NaN   \n",
       "1    http://www.courierpostonline.com/article/20140...                  5.0   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...                  NaN   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...                  NaN   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...                  5.0   \n",
       "..                                                 ...                  ...   \n",
       "604  http://philadelphia.cbslocal.com/2018/03/04/po...                181.0   \n",
       "605  https://www.courierpostonline.com/story/news/c...                  5.0   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...                175.0   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...                175.0   \n",
       "608  http://philadelphia.cbslocal.com/2018/03/29/ca...                  5.0   \n",
       "\n",
       "    state_senate_district    datetime  year state_pop   city_pop  \\\n",
       "0                     NaN  2014-01-01  2014  12788313  1565460.0   \n",
       "1                     5.0  2014-01-01  2014   8864525    75673.0   \n",
       "2                     NaN  2014-01-02  2014  12788313  1565460.0   \n",
       "3                     NaN  2014-01-02  2014  12788313  1565460.0   \n",
       "4                     5.0  2014-01-02  2014   8864525    75673.0   \n",
       "..                    ...         ...   ...       ...        ...   \n",
       "604                   1.0  2018-03-04  2018  12800922  1583592.0   \n",
       "605                   5.0  2018-03-05  2018   8886025    73725.0   \n",
       "606                   1.0  2018-03-10  2018  12800922  1583592.0   \n",
       "607                   1.0  2018-03-15  2018  12800922  1583592.0   \n",
       "608                   5.0  2018-03-29  2018   8886025    73725.0   \n",
       "\n",
       "     area (sq. mi)  pop_density  \n",
       "0            46058   277.656715  \n",
       "1             8722  1016.340862  \n",
       "2            46058   277.656715  \n",
       "3            46058   277.656715  \n",
       "4             8722  1016.340862  \n",
       "..             ...          ...  \n",
       "604          46058   277.930479  \n",
       "605           8722  1018.805893  \n",
       "606          46058   277.930479  \n",
       "607          46058   277.930479  \n",
       "608           8722  1018.805893  \n",
       "\n",
       "[609 rows x 36 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phil_camden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
    "id": "NBAuOI6DCsRO",
    "outputId": "26ba7182-d23d-4770-f513-43df2aecf52b"
   },
   "outputs": [
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       "      <td>46058</td>\n",
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       "      <td>45300</td>\n",
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       "      <td>0</td>\n",
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       "      <td>http://6abc.com/parking-lot-attendant-shot-dur...</td>\n",
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       "      <td>175.0</td>\n",
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       "      <td>45300</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/107...</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.courierpostonline.com/story/news/c...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2018-03-29</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725.0</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "      <td>26214</td>\n",
       "      <td>0.093</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  "
      ],
      "text/plain": [
       "     Unnamed: 0  incident_id        date         state city_or_county  \\\n",
       "0           288        92423  2014-01-01  Pennsylvania   Philadelphia   \n",
       "1           297        92747  2014-01-01    New Jersey         Camden   \n",
       "2           532        92349  2014-01-02  Pennsylvania   Philadelphia   \n",
       "3           557       220357  2014-01-02  Pennsylvania   Philadelphia   \n",
       "4           604        92727  2014-01-02    New Jersey         Camden   \n",
       "..          ...          ...         ...           ...            ...   \n",
       "604      235382      1062115  2018-03-04  Pennsylvania   Philadelphia   \n",
       "605      235559      1063484  2018-03-05    New Jersey         Camden   \n",
       "606      236316      1066841  2018-03-10  Pennsylvania   Philadelphia   \n",
       "607      237129      1070055  2018-03-15  Pennsylvania   Philadelphia   \n",
       "608      239385      1080442  2018-03-29    New Jersey         Camden   \n",
       "\n",
       "                              address  n_killed  n_injured  \\\n",
       "0    34th Street and Haverford Avenue         0          1   \n",
       "1                                 NaN         0          1   \n",
       "2                          7th Street         0          1   \n",
       "3           34th and Haverford Avenue         0          1   \n",
       "4          1600 block of Louis Street         1          0   \n",
       "..                                ...       ...        ...   \n",
       "604     N 6th St and Spring Garden St         1          1   \n",
       "605          400 block of Mechanic St         1          0   \n",
       "606           200 block of Lombard St         0          1   \n",
       "607      700 block of S Columbus Blvd         0          0   \n",
       "608         Kaighn Ave and Haddon Ave         0          4   \n",
       "\n",
       "                                          incident_url  \\\n",
       "0     http://www.gunviolencearchive.org/incident/92423   \n",
       "1     http://www.gunviolencearchive.org/incident/92747   \n",
       "2     http://www.gunviolencearchive.org/incident/92349   \n",
       "3    http://www.gunviolencearchive.org/incident/220357   \n",
       "4     http://www.gunviolencearchive.org/incident/92727   \n",
       "..                                                 ...   \n",
       "604  http://www.gunviolencearchive.org/incident/106...   \n",
       "605  http://www.gunviolencearchive.org/incident/106...   \n",
       "606  http://www.gunviolencearchive.org/incident/106...   \n",
       "607  http://www.gunviolencearchive.org/incident/107...   \n",
       "608  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                            source_url  ...  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...  ...   \n",
       "1    http://www.courierpostonline.com/article/20140...  ...   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...  ...   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...  ...   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...  ...   \n",
       "..                                                 ...  ...   \n",
       "604  http://6abc.com/1-killed-1-injured-in-spring-g...  ...   \n",
       "605  http://www.nj.com/camden/index.ssf/2018/03/man...  ...   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...  ...   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...  ...   \n",
       "608  https://www.courierpostonline.com/story/news/c...  ...   \n",
       "\n",
       "     state_house_district  state_senate_district    datetime  year state_pop  \\\n",
       "0                     NaN                    NaN  2014-01-01  2014  12788313   \n",
       "1                     5.0                    5.0  2014-01-01  2014   8864525   \n",
       "2                     NaN                    NaN  2014-01-02  2014  12788313   \n",
       "3                     NaN                    NaN  2014-01-02  2014  12788313   \n",
       "4                     5.0                    5.0  2014-01-02  2014   8864525   \n",
       "..                    ...                    ...         ...   ...       ...   \n",
       "604                 181.0                    1.0  2018-03-04  2018  12800922   \n",
       "605                   5.0                    5.0  2018-03-05  2018   8886025   \n",
       "606                 175.0                    1.0  2018-03-10  2018  12800922   \n",
       "607                 175.0                    1.0  2018-03-15  2018  12800922   \n",
       "608                   5.0                    5.0  2018-03-29  2018   8886025   \n",
       "\n",
       "      city_pop area (sq. mi)  pop_density  median_income unemployment  \n",
       "0    1565460.0         46058   277.656715          45300        0.072  \n",
       "1      75673.0          8722  1016.340862          26214        0.093  \n",
       "2    1565460.0         46058   277.656715          45300        0.072  \n",
       "3    1565460.0         46058   277.656715          45300        0.072  \n",
       "4      75673.0          8722  1016.340862          26214        0.093  \n",
       "..         ...           ...          ...            ...          ...  \n",
       "604  1583592.0         46058   277.930479          45300        0.072  \n",
       "605    73725.0          8722  1018.805893          26214        0.093  \n",
       "606  1583592.0         46058   277.930479          45300        0.072  \n",
       "607  1583592.0         46058   277.930479          45300        0.072  \n",
       "608    73725.0          8722  1018.805893          26214        0.093  \n",
       "\n",
       "[609 rows x 38 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phil_camden['median_income'] = 26214\n",
    "phil_camden.loc[phil_camden['city_or_county'] == 'Philadelphia', 'median_income'] = 45300\n",
    "\n",
    "phil_camden['unemployment'] = 0.093\n",
    "phil_camden.loc[phil_camden['city_or_county'] == 'Philadelphia', 'unemployment'] = 0.072\n",
    "\n",
    "phil_camden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W7M7RtcSCsRP"
   },
   "outputs": [],
   "source": [
    "def parse_age(age_string):\n",
    "    if type(age_string) != str:\n",
    "        return None\n",
    "    \n",
    "    ages = []\n",
    "    age_split = []\n",
    "    if '||' in age_string:\n",
    "        age_split = age_string.split('||')\n",
    "    elif '|' in age_string:\n",
    "        age_split = age_string.split('|')\n",
    "    else:\n",
    "        age_split = [age_string]\n",
    "    \n",
    "    for age in age_split:\n",
    "        if '::' in age:\n",
    "            ages.append(int(age.split('::')[-1]))\n",
    "        elif ':' in age:\n",
    "            ages.append(int(age.split(':')[-1]))\n",
    "        else:\n",
    "            return None\n",
    "\n",
    "    return np.mean(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zz3xyHc3CsRP"
   },
   "outputs": [],
   "source": [
    "phil_camden['mean_participant_age'] = phil_camden['participant_age'].apply(parse_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
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    "id": "QJgqQTyECsRQ",
    "outputId": "97bbd04c-7926-48ff-f4ba-de6bb0336c40",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     y  T  I  T*I  median_income  pop_density  unemployment  \\\n",
       "0    1  1  0    1          45300   277.656715         0.072   \n",
       "3    1  1  0    1          45300   277.656715         0.072   \n",
       "4    1  0  0    0          26214  1016.340862         0.093   \n",
       "5    1  1  0    1          45300   277.656715         0.072   \n",
       "6    1  1  0    1          45300   277.656715         0.072   \n",
       "..  .. .. ..  ...            ...          ...           ...   \n",
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       "606  1  1  1    1          45300   277.930479         0.072   \n",
       "\n",
       "     mean_participant_age  \n",
       "0                    18.0  \n",
       "3                    18.0  \n",
       "4                    39.0  \n",
       "5                    19.0  \n",
       "6                    31.0  \n",
       "..                    ...  \n",
       "602                  59.0  \n",
       "603                  17.0  \n",
       "604                  32.0  \n",
       "605                  41.0  \n",
       "606                  51.0  \n",
       "\n",
       "[465 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_df = phil_camden[['year', 'city_or_county', 'n_killed', 'n_injured', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age']]\n",
    "did_df = did_df.dropna()\n",
    "\n",
    "did_df['T'] = 0\n",
    "did_df.loc[did_df['city_or_county'] == 'Philadelphia', 'T'] = 1\n",
    "\n",
    "did_df['I'] = 0\n",
    "did_df.loc[did_df['year'] > 2014, 'I'] = 1\n",
    "\n",
    "did_df['y'] = did_df['n_killed'] + did_df['n_injured']\n",
    "\n",
    "did_df['T*I'] = did_df['T']\n",
    "\n",
    "did_df = did_df[['y', 'T', 'I', 'T*I', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age']]\n",
    "did_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-BxdHNb-CsRQ"
   },
   "source": [
    "Using OLS, we can find the equivalent value of the difference-in-difference estimate of the policy effect. That is, we can find the mean number of deaths before and after Act 192 in the Philadelphia treatment group, and the mean number of deaths before and after Act 192 in the Camden control group, and calculate the difference between these means. This is because our averages are just expected values from our estimator.\n",
    "\n",
    "The reason we use OLS is to easily include our four confounders as well as calculating errors.\n",
    "\n",
    "To do this, we can estimate the unknown parameters in $y = \\beta_0 + \\beta_1 T + \\beta_2 I + \\beta_3 TI + \\beta_4 X_1 + \\beta_5 X_2 + \\beta_6 X_3 + \\beta_7 X_4$ where $X_i$ is the $i^{th}$ confounder in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zjllI2PGCsRR",
    "outputId": "22769a06-eed6-4126-ffd0-72c1b1ba036d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T                       80.069005\n",
       "I                        0.176325\n",
       "T*I                     80.069005\n",
       "median_income           -0.004166\n",
       "pop_density              0.108780\n",
       "unemployment            -7.103068\n",
       "mean_participant_age    -0.001602\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = did_df.loc[:, did_df.columns != 'y']\n",
    "Y = did_df['y']\n",
    "\n",
    "model = sm.OLS(Y, X)\n",
    "results = model.fit()\n",
    "results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D7pVwQRrCsRR"
   },
   "source": [
    "We can visualize this result by plotting the mean number of injuries + deaths pre and post intervention, segregated by treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "IMIJg7v8CsRR",
    "outputId": "4145feff-98f0-4d3b-f3fa-c144ed937bda"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mJN9Me+RES9KjyNltLfKDDbKTSgpOWubp2DOlIJKtJR8REekTCjUZpDHa2mkWpbahw3JPlwZvdd20yAcYWZDbHkjOHltE6dTSbnumFOfrKLKIiAwMCjUDWLQ1HmyeTfRMadss27XzbG19lBMtyY8iF0ay22dMJowcxocmjuhyA8JgZiXYl5KrFvkiIjIIKdT0o1jccaQxesrOs+339qlv5lh3LfKzs9pDSGnQ2K20wyxK+318gs/VIl9ERIYChZpecM5xvLk1CCI9d56ta4iSbMXHt8hPzJjMGl/sg0mynilFEYarRb6IiMhJegw1ZvYQ8FHggHPu3CSPzwB+CJwPfNU5d1/aq+xHTS2xTmHkVJ1na+ujRGPJjyIX5+dQVuRP9FSUFTC/YlS3PVNG6iiyiIhIr6UyU/Mw8D3gkW4erwP+CvhYmmpKq9ZYnLr2uyKf3Hn2UJdloIZuWuTn52a1z56MGZ7PzHHFiT0pHTrPtgUZtcgXERHpXz2GGufcM2ZWcYrHDwAHzOwjaazrjMXjjlX/e1P7DMuRxuQt8nOyrD2MlBVFmFxS0On4cdeeKWqRLyIiMrD16zu1ma0GVgOUl5f3yWtkZRlFeTmJzbKFeZQURTot/ZQV5lE8TPtSREREMkm/hhrn3FpgLUBVVVXyJilp8M83X9BXlxYREZEBShs/REREJCMo1IiIiEhGSOVI98+AxUCZmdUAXwdyAZxzPzCzcUA1UAzEzexOYJZz7lifVS0iIiLSRSqnn1b18Pj7wMS0VSQiIiJyBrT8JCIiIhlBoUZEREQygkKNiIiIZASFGhEREckICjUiIiKSERRqREREJCMo1IiIiEhGUKgRERGRjKBQIyIiIhlBoUZEREQygkKNiIiIZASFGhEREckICjUiIiKSERRqREREJCMo1IiIiEhGUKgRERGRjKBQIyIiIhlBoUZEREQygkKNiIiIZASFGhEREckICjUiIiKSERRqREREJCMo1IiIiEhGUKgRERGRjKBQIyIiIhlBoUZEREQygkKNiIiIZIQeQ42ZPWRmB8zsjW4eNzP7rpltN7PXzOz89JcpIiIicmqpzNQ8DKw8xeNXAtODX6uBf+p9WSIiIiKnp8dQ45x7Bqg7xVOuAR5x3iZgpJmdla4CRURERFKRjj01E4B9HT6vCcZERERE+k2/bhQ2s9VmVm1m1QcPHuzPlxYREZEMl45Qsx+Y1OHzicHYSZxza51zVc65qtGjR6fhpUVERES8dISadcCng1NQFwFHnXPvpeG6IiIiIinL6ekJZvYzYDFQZmY1wNeBXADn3A+A3wL/CdgONAKf7atiRURERLrTY6hxzq3q4XEH/GXaKhIRERE5A+ooLCIiIhlBoUZEREQygkKNiIiIZASFGhEREckICjUiIiKSERRqREREJCMo1IiIiEhGUKgRERGRnjUdg63roOVE2JV0q8fmeyIiIjIEOQcH34Zt62HbBti7EeKtcOMvYPqKsKtLSqFGREREvOZ62PUMbN/gg8zRfX587Llw8R0wbQVMWhBujaegUCMiIjJUOQe1O4LZmPWw53mIRSFSBFMXw2Vf8kFmxISwK02JQo2IiMhQ0nICdj+XCDKHd/vxsnNgwWqYfgWUL4ScSKhlngmFGhERkUxXt8svJ23f4JeXWpsgZxhMvTyxrDRqcthV9ppCjYiISKZpbYY9L/ggs2091G7z4yWVMP9mv9F38iLIzQ+1zHRTqBEREckER/YlNvjufBpaGiA7DyoWwQWf80GmtDLsKvuUQo2IiMhgFGuBvZv8TMz2x+DAVj8+ohzOW+X3xlQsgkhhuHX2I4UaERGRweLYez7AbFsPO5+C5mOQlQuTL4Yr7vZBpuxsMAu70lAo1IiIiAxUsVbYX504qfT+6358+HiYfa0PMVMvh7zh4dY5QCjUiIiIDCT1BxOzMTuegKYjYNlQfhEsX+ODzJhZQ3Y25lQUakRERMIUj8O7WxKzMe9uARwUjoEZH4Xpy2HqEhg2MuxKBzyFGhERkf7WWOdnYbath+2PQ+MhwGDiBbDkq/6k0ri5kKX7Tp8OhRoREZG+Fo/D+68ljlzXvAQuDgWlMG25b35XuRQKS8OudFBTqBEREekLTUdhx5OJTr71H/jx8fPgsi/7vTHj50FWdrh1ZhCFGhERkXRwzveK2bbeB5m9m8DFIH8EVC7zIWbaMigaE3alGUuhRkRE5Ew1H/fde9uWlY7t9+Pj5sCiO/2y0sQLIFtvt/1BP2UREZFUOQeHtiVOKu15AeItEBkOlYth8Vf8Hpni8WFXOiQp1IiIiJxKtBF2P5sIMkf2+vHRM+GiL/hlpUkXQk4k3DpFoUZEROQktTsSG3x3PQuxZsgtgKmLYdEX/bLSyElhVyldpBRqzGwl8I9ANvCgc+6eLo9PBh4CRgN1wE3OuZo01yoiItI3Wppgz/M+yGxbD3U7/HjpdLjgVt83ZvIlkJMXbp1ySj2GGjPLBr4PrABqgJfMbJ1zbmuHp90HPOKc+5GZLQW+BXyqLwoWERFJi8N7Eht8dz0DLY2Qkw8Vl8KFf+E7+ZZMDbtKOQ2pzNQsALY753YCmNnPgWuAjqFmFnBX8PGTwK/SWaSIiEivtUZh78agi+9jcPBtPz5yMsy7ye+NqVgEucPCrVPOWCqhZgKwr8PnNcCFXZ7zKvBn+CWqa4HhZlbqnKvt+CQzWw2sBigvLz/TmkVERFJz7N3EktLOpyBaD9kRv5R0/qd9kCmdpptDZoh0bRT+EvA9M7sZeAbYD8S6Psk5txZYC1BVVeXS9NoiIiJerBVq/phogPfBG368eCLMuc6HmCmXQV5RuHVKn0gl1OwHOm7xnhiMtXPOvYufqcHMioCPO+eOpKtIERGRbh3/wC8nbVvvb0vQfBSycqB8Iaz4hg8yo2doNmYISCXUvARMN7Mp+DBzA/DJjk8wszKgzjkXB/4GfxJKREQk/eIx2L85saz03it+vGgczLrKh5ipi/3tCWRI6THUOOdazex24A/4I90POefeNLNvANXOuXXAYuBbZubwy09/2Yc1i4jIUNNQCzseDzb5Pg4n6sCyYOICWPp3PsiMm6PZmCHOnAtna0tVVZWrrq4O5bVFRGSAi8f9DEzbslJNNeCgoMz3jJm2HCqXQkFJ2JVKGpnZZudc1Zl+vToKi4jIwHDisN8T09bJt+EgYDDhfH9Ppekr4Kx5kJUVdqUyQCnUiIhIOJzzp5PaTirt+yO4GOSP9DMx06+AacugsCzsSmWQUKgREZH+03TM94tp6+R7/D0/ftaH4NK7/D2VJlZBVnaoZcrgpFAjIiJ9xzk4+KfEHa73boR4K+QVQ+WSYDZmOQwfF3alkgEUakREJL2iDf5eSm3LSkeDpvRjZsPC232QmbQAsnPDrVMyjkKNiIj0jnNQuyM4br0Bdj8HsSjkFvrZmMu+5GdjRkwMu1LJcAo1IiJy+lpOwO7nE8tKh3f58bJzYMFqf1KpfCHk5IVbpwwpCjUiIpKaw7sTXXx3PQutJyBnmL+X0sK/9EFmVEXYVcoQplAjIiLJtTbDnhcSfWMOvePHR02B+Z/xIWbyJZA7LNw6RQIKNSIiknC0JpiN2eCPXrc0QHYeVCyCqlv8Jt/SyrCrFElKoUZEZCiLtcC+FxMnlQ5s9eMjyuFDN/gQM+VSiBSGW6dIChRqRESGmuPvJ/bG7HwKmo9BVg5MvhiuuNsHmbKzdXNIGXQUakREMl2sFfZXJ4LM+6/58eFnweyPBbMxl0N+cbh1ivSSQo2ISCaqPwg7Hg96xzwOTUfAsmHShbDs6z7IjJ2t2RjJKAo1IiKZIB6Hd7ckGuDtfxlwUDgGZnzEN7+rXALDRoVdqUifUagRERmsGutgxxPBkevHoPEQYP6GkEv+uz9yPe5DkJUVdqUi/UKhRkRksHDO74dpO6lU8xK4OAwr8TMx06+AyqVQWBp2pSKhUKgRERnImo7Cjif9ktK2x6D+fT8+fh5c9mWYtgImnA9Z2eHWKTIAKNSIiAwkzsGBtxKzMfs2QbwV8kf4WZjpV/hZmaIxYVcqMuAo1IiIhK25HnY9nQgyx/b78bFz4OK/8kFm4gWQrb+yRU5F/4eIiPQ35+DQtsRJpT0vQCwKkeFQuRgWf8XPxhSPD7tSkUFFoUZEpD9EG2H3c8FszHo4ssePj54JF/65n42ZdBHkRMKtU2QQU6gREekrdTsTXXx3PwetTZBb4Lv3XvJf/JHrkeVhVymSMRRqRETSpaUJ9jwf9I3ZALXb/XjptOAO1yug/GLIzQ+3TpEMpVAjItIbR/YGszEb/GbflkbIyYeKS2HBar83prQy7CpFhgSFGhGR09Ea9ces204qHXzbj48sh/Nu9HtjKhZBpCDcOkWGIIUaEZGeHHs3sTdm59MQPQ5ZuVBxCZz/ad8Ar2y6bg4pErKUQo2ZrQT+EcgGHnTO3dPl8XLgR8DI4Dlfcc79Ns21ioj0j1gr1Pwxsaz0wet+vHgCzPm4n42ZchnkDQ+3ThHppMdQY2bZwPeBFUAN8JKZrXPObe3wtL8F/tU5909mNgv4LVDRB/WKiPSN+gP+ppDb1vubRDYdBcuG8oWw/O99kBkzU7MxIgNYKjM1C4DtzrmdAGb2c+AaoGOocUBx8PEI4N10FikiknbxGOx/OdEA790tfrxoLMy8yoeYqYv97QlEZFBIJdRMAPZ1+LwGuLDLc9YA683sDqAQWJ7sQma2GlgNUF6u3gwi0s8a62D740GQeQxO1IFl+VsQLP1bH2TGzoGsrLArFZEzkK6NwquAh51z3zazhcCPzexc51y845Occ2uBtQBVVVUuTa8tIpJcPA7vv5rY5FtTDTgoKPUBZvoKf5PIgpKwKxWRNEgl1OwHJnX4fGIw1tGtwEoA59xGM8sHyoAD6ShSRCRlJ474PTHbH/NhpuEAYDDh/OCeSitg/DzNxohkoFRCzUvAdDObgg8zNwCf7PKcvcAy4GEzmwnkAwfTWaiISFLOwQdvJvrG7HsRXAzyR8K0ZX5GpnIZFI0Ou1IR6WM9hhrnXKuZ3Q78AX9c+yHn3Jtm9g2g2jm3Dvhr4H+b2Rfxm4Zvds5peUlE+kbzcdj5VBBkHoPjwdmEcXNh0Rd9kJkwH7LViktkKEnp//ig58xvu4x9rcPHW4FL0luaiEjAOTj4p8RJpT0bId4CecVQucSHmGnLYfi4sCsVkRDpnzEiMjBFG2DXs4llpaN7/fiY2bDwNh9kJl0I2bnh1ikiA4ZCjYgMHLU7ghCzHnY/D7FmyC30/WIuvcufVhoxMewqRWSAUqgRkfC0nPDhpW1ZqW6nHy87GxZ83oeY8oWQkxdunSIyKCjUiEj/Orw7cU+lXc9A6wnIGQZTLoWLbvN7Y0qmhF2liAxCCjUi0rdam2HvxkQDvEPv+PFRFf4O19Ov8He7zh0WapkiMvgp1IhI+h2t6TAb8zRE6yE7AhWLoOoW3wCvtFI3hxSRtFKoEZHei7X4pndtQebAm358xCSY+wk/GzPlMogUhluniGQ0hRoROTPH3w9uRbAedjwJzccgK8dv7F3xP3yQGX2OZmNEpN8o1IhIauIxf0PItiPX77/mx4efBbM/FszGXA75xeHWKSJDlkKNiHSv4RBsfzyYjXkcThwGy/ZN75Z9zQeZsedqNkZEBgSFGhFJiMfhvS2Jk0r7XwYcFI6Gs6/0fWMql8CwUWFXKiJyEoUakaGusQ52PBHsj9kAjYcAg4lVsOS/+yAz7kOQlRV2pSIip6RQIzLUOAfvv564p1LNH8HFYVgJTFvml5Qql0FhadiVioicFoUakaGg6SjsfCoIMo9B/ft+/Kzz4NIv+SAz4XzIyg61TBGR3lCoEclEzsGBt4J7Kj3mO/rGWyFvBExbmpiNGT427EpFRNJGoUYkUzTX+3sptS0rHavx42PnwMV3+CAzcQFk6397EclM+ttNZLByDmq3J/rG7HkBYlGIFMHUxXD5f/U3hxwxIexKRUT6hUKNyGASbYTdzwXLShv8Ha8BRs+AC//cz8ZMughyIqGWKSISBoUakYGubqff3LttPex+FlqbILfA30vp4jv8zSFHTQ67ShGR0CnUiAw0rc2w5/lEA7za7X68pGV93FMAAA/2SURBVBLmf9b3jZl8CeTmh1uniMgAo1AjMhAc2Zu4w/Wup6GlEbLzYMqlsGC13xtTWhl2lSIiA5pCjUgYWqOwb1MiyBx8y4+PLIfzPun3xlRcCpGCcOsUERlEFGpE+sux9/zm3m3rYcdTED0OWbkw+WKYd5MPMmXTdXNIEZEzpFAj0ldirVDzUqJvzAev+/HiCTDn4z7ETLkM8oaHW6eISIZQqBFJp/oDiRtD7njc357AsqF8ISxf44PMmFmajRER6QMKNSK9EY/Bu1sSDfDe3eLHi8bCjKv8SaWpi2HYyDCrFBEZEhRqRE5XYx1sf9zvj9n+GDTWgmXBxAtg6d/62ZixcyArK+xKRUSGFIUakZ7E4/D+a4m+MfurwcWhoNQ3vpu+AiqXQkFJ2JWKiAxpKYUaM1sJ/COQDTzonLuny+PfAZYEnxYAY5xzmm+XwevEEdj5ZOLIdcMBwGD8PLjsv/rZmPHnQVZ22JWKiEigx1BjZtnA94EVQA3wkpmtc85tbXuOc+6LHZ5/BzCvD2oV6TvOwQdvBvdUegz2bgIXg/yRMG2ZDzGVy6BodNiViohIN1KZqVkAbHfO7QQws58D1wBbu3n+KuDr6SlPpA81H4edTyeOXB9/14+PmwuL7vRBZkIVZGuVVkRkMEjlb+sJwL4On9cAFyZ7oplNBqYAT3Tz+GpgNUB5eflpFSrSa87BoXcSJ5X2bIR4C0SGQ+USH2KmLYfis8KuVEREzkC6/wl6A/AL51ws2YPOubXAWoCqqiqX5tcWOVm0AXY9GywrbfD3WALfK2bhbX6jb/lFkJ0bbp0iItJrqYSa/cCkDp9PDMaSuQH4y94WJdIrtTsSJ5V2PwexZsgthKmXw6Iv+iAzclLP1xERkUEllVDzEjDdzKbgw8wNwCe7PsnMZgCjgI1prVCkJy1NsOe5RJCp2+nHS6fDBZ/zR64nXww5eeHWKSIifarHUOOcazWz24E/4I90P+Sce9PMvgFUO+fWBU+9Afi5c07LStL3Du9JbPDd9Qy0noCcfH8vpYtu83tjSqaEXaWIiPQjCyuDVFVVuerq6lBeWwah1ijsfSHRN+bQn/z4qAq/wXf6FVCxCHKHhVqmiIicOTPb7JyrOtOv11lVGbiO7vebe7dtgJ1PQbQesiMw+RKYf7MPMqWVujmkiIgACjUykMRaYN8fE8tKB9704yMmwdxPBLMxl0JeUbh1iojIgKRQI+E6/r7v4LttA+x4EpqPQlYOlC+EFd/wQWb0DM3GiIhIjxRqpH/FY7B/c6IB3nuv+vGicTDrah9ipi6G/OIwqxQRkUFIoUb6XsMh2P64DzE7HocTh8GyYNKFsOxrPsiMPVezMSIi0isKNZJ+8Ti890qib8z+zYCDwtFw9pUwfTlULoVho8KuVEREMohCjaTHicOw4wkfZLY/Bg0HAYMJ82Hx3/gGeGedB1lZYVcqIiIZSqFGzoxz8P7rwT2VHoN9L4KL+9mXacv9klLlUigsC7tSEREZIhRqJHVNx3y/mLYgc/w9P37WeXDpX/sgM2E+ZGWHWqaIiAxNCjXSPefg4NuJvjF7N0K8FfJGQOUSH2KmLYfhY8OuVERERKFGumiu9/dSapuNObrPj489Fy6+w9/hetICyM4Nt04REZEuFGqGOuegdnvipNKe5yEWhUiR7xdz2Zd8kBkxIexKRURETkmhZihqOQG7n0s0wDu824+XnQMLVvtlpfKFkBMJtUwREZHToVAzVNTtSszG7H4WWpsgZxhMvTyxrDRqcthVioiInDGFmkzV2uyXkrY95oNM7TY/XlIZ3OF6BUxeBLn5oZYpIiKSLgo1meTIPti+wc/I7HwaWhogOw8qFsEFn/NBprQy7CpFRET6hELNYBZrgb2bEkeuD77lx0eUw3mr/N6YikUQKQy3ThERkX6gUDPYHHuvw2zMU9B8DLJyYfLFMO9GH2TKztbNIUVEZMhRqBnoYq2wvzpxUun91/348PEw+1ofYqZeDnnDw61TREQkZAo1A1H9Qd/4btt62PE4NB0Fy4byi2D5Gh9kxszSbIyIiEgHCjUDQTwG725JHLl+dwvgoHAMzLgKpi+HqUtg2MiwKxURERmwFGrC0lgHO55I3I6gsRYsCyZUwZKv+pNK4+ZCVlbYlYqIiAwKCjX9JR6H91/zszHbN0DNS+DiUFDqbwo5/QqoXAoFJWFXKiIiMigp1PSlpqOw48lEkKn/wI+PPx8u+7IPMuPnQVZ2uHWKiIhkAIWadHIODmxN9I3ZuwlcDPJHQOUyH2KmLYOiMWFXKiIiknEUanqr+bjv3tu2N+bYfj8+bg4sutPfU2niBZCtH7WIiEhf0jvt6XIODr2TOKm05wWIt0BkOFQuhsVf8XtkiseHXamIiMiQklKoMbOVwD8C2cCDzrl7kjznE8AawAGvOuc+mcY6wxVt9He2bmuAd2SvHx89Ey76gl9WmnQh5ETCrVNERGQI6zHUmFk28H1gBVADvGRm65xzWzs8ZzrwN8AlzrnDZjb4N43U7kjMxux+DmLNkFsAUxfDoi/6ZaWRk8KuUkRERAKpzNQsALY753YCmNnPgWuArR2e83ng+865wwDOuQPpLrTPtTTBnudgW9DJt26HHy+dDhfc6vvGTL4EcvLCrVNERESSSiXUTAD2dfi8Briwy3POBjCz5/FLVGucc7/veiEzWw2sBigvLz+TetPr8J7EzSF3PQMtjZCTDxWXwoV/4Tv5lkwNu0oRERFJQbo2CucA04HFwETgGTOb45w70vFJzrm1wFqAqqoql6bXTl1rFPZuTBy5PvQnPz5yMsy7ye+NqVgEucP6vTQRERHpnVRCzX6g4+aRicFYRzXAi865FmCXmb2DDzkvpaXK3ji6PzEbs/MpiNZDdsQvJc3/jA8ypdN0c0gREZFBLpVQ8xIw3cym4MPMDUDXk02/AlYBPzSzMvxy1M50FpqyeAz2vZiYjfngDT9ePBHmXOdDzJTLIK8olPJERESkb/QYapxzrWZ2O/AH/H6Zh5xzb5rZN4Bq59y64LErzGwrEAO+7Jyr7cvCuxVrgX/5OMSiUL4QVnzDB5nRMzQbIyIiksHMuf7f2gJ+T011dXXfXHzvJhgz09+eQERERAYFM9vsnKs606/PzI7C5ReFXYGIiIj0s6ywCxARERFJB4UaERERyQgKNSIiIpIRFGpEREQkIyjUiIiISEZQqBEREZGMoFAjIiIiGUGhRkRERDKCQo2IiIhkhNBuk2BmB4E9ffgSZcChPry+iIjIUNPX762TnXOjz/SLQws1fc3Mqntz/wgRERHpbKC/t2r5SURERDKCQo2IiIhkhEwONWvDLkBERCTDDOj31ozdUyMiIiJDSybP1IiIiMgQolAjIiIiGSFtocbMYmb2ipm9amYvm9nFKXzNX5nZW2b2k3TV0eX6/5+Z7TezU36fZjbSzG47xeMPmdkBM3ujy/iHzGyjmb1uZr82s+JgfIWZbQ7GN5vZ0vR8RyIiImBmHzMzZ2YzOoyNNrMXzWyLmV16qve103ytMjNrMbO/SLGuWd08dlmQD1rN7D93eewfzOyN4Nf1HcZ/YmZ/CsYfMrPcU71+OmdqTjjnznPOfQj4G+BbKXzNbcAK59yNqbyAmeWkWkwQZK4F9gGX9/D0kUEt3XkYWJlk/EHgK865OcAvgS8H44eAq4LxzwA/TrVuERGRFKwCngt+b7MMeN05Nw//3ndaoca8ZLngOmBTl9fqzseApKEG2AvcDPy0y+t+BDgfOA+4EPhS2yQB8BNgBjAHGAZ87lQv3lfLT8XA4bZPzOzLZvaSmb1mZn8fjP0AmAr8zsy+aGYlZvar4DmbzGxu8Lw1ZvZjM3se+HGQRB8NrveSmV3STQ2LgTeBf6LDfwgzG2tmvwxmlF4NZpTuASqDmab/2fVCzrlngLokr3E28Ezw8Qbg48Hztzjn3g3G3wSGmVleKj84ERGRUzGzImARcCtwQzB2HnAvcI2ZvQL8A13e17p5L64IZkIeAd4AJiV5yVXAXwMTzGxihzo+HVzr1eB9+mLgauB/Bq9b2fEizrndzrnXgHiX688CnnHOtTrnGoDXCCYSnHO/dQHgj8BETiHlmY8UDAt+kPnAWcBSADO7ApgOLAAMWGdmlznn/sLMVgJLnHOHzOx+YItz7mPBcs0j+NTW9g0vcs6dMLOfAt9xzj1nZuXAH4CZSepZBfwM+D/A/2Nmuc65FuC7wNPOuWvNLBsoAr4CnOucOy/JdU7lTeAa4Ff4JJvsD8PHgZedc82neW0REZFkrgF+75x7x8xqzWy+c26zmX0NqHLO3W5mFcDstve17t6L8bMn04HPOOc2dX0hM5sEnOWc+6OZ/StwPfBtM5sN/C1wcfAeXuKcqzOzdcBvnHO/OI3v51Xg62b2baAAWAJs7VJHLvAp4L+c6kJ9sfw0A5+wHjEzA64Ifm0BXsZPI01P8vWLCJZpnHNPAKUdpp/WOedOBB8vB74XBKh1QHGQWtuZWQT4T8CvnHPHgBeBDwcPL8XP3uCciznnjvbie74FuM3MNgPDgWiXOmbj0/Kf9+I1REREOloF/Dz4+Oektix0qvfiPckCTeB64F+TvNZS4N+cc4cAnHPJVjNS4pxbD/wWeAE/GbERiHV52gP42ZxnT3WtdM7UdCxwo5mVAaPxifBbzrn/1YtLNnT4OAu4yDnXdIrnfxi/T+Z1n6soAE4Av+lFDSdxzr2N/0OCmZ0NfKTtsWCK7pfAp51zO9L5uiIiMjSZWQk+UMwxMwdkA87Mvnzqr0z+XhzM6DQk+4LAKmCcmbXtfR1vZskmJnrFOfdN4JtBTT8F3ulQ49fxeaLHCYI+2VMT7MbOBmrxy0O3tM2mmNkEMxuT5MueBW4MnrMYOBTMsnS1Hrijw2slWzJaBXzOOVfhnKsApgArzKwAeBz4QvC12WY2AjiOn2k53e9zTPB7Fn4a7gfB5yOB/8BvIn7+dK8rIiLSjf8M/Ng5Nzl4j5sE7AIu7fK8ru9rqb4Xtwv+sV7knJvQ4f30W/j32CeA68ysNHhuSTev26PgvbjtOnOBufj3eszsc/iJilXOua57cU6SzlAzLNgY9Arw/+PX52LBtNJPgY1m9jrwC5J/w2uA+Wb2Gn7j7me6eZ2/AqqCzUlbgU5HzILgshIfKgAINh49B1yFX49bEtSyGZjlnKsFnjd/ZOykjcJm1jYddo6Z1ZjZrcFDq8zsHeBt4F3gh8H47cA04GttP5Oe/vCIiIikYBV+FaCjR+myBNX1fe003otTei3n3Jv4mZWnzexV4P8NHv858GXzx8o7bRQ2swvMrAa/B/V/mdmbwUO5wLPBe/pa4CbnXGvw2A+AsUHdrwT7hrql2ySIiIhIRlBHYREREckICjUiIiKSERRqREREJCMo1IiIiEhGUKgRERGRjKBQIyIiIhlBoUZEREQywv8F5dMu+NulanoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "did_df_treatment = did_df[did_df['T'] == 1]\n",
    "did_df_control = did_df[did_df['T'] == 0]\n",
    "\n",
    "mean_treatment_before = np.mean(did_df_treatment[did_df_treatment['I'] == 0]['y'])\n",
    "mean_treatment_after = np.mean(did_df_treatment[did_df_treatment['I'] == 1]['y'])\n",
    "\n",
    "mean_control_before = np.mean(did_df_control[did_df_control['I'] == 0]['y'])\n",
    "mean_control_after = np.mean(did_df_control[did_df_control['I'] == 1]['y'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4), tight_layout=True)\n",
    "plt.plot([0, 1], [mean_treatment_before, mean_treatment_after], label='philly')\n",
    "ax.set_xticks([0, 1])\n",
    "plt.plot([0, 1], [mean_control_before, mean_control_after], label='camden')\n",
    "ax.set_xticklabels(['Before Act 192', 'After Act 192'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4w1NQC6oD3Q0"
   },
   "source": [
    "**Simple Version of Difference-in-Difference Calculation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gi23kJKACsRS",
    "outputId": "c7d3110d-cd70-4b57-eb97-4ca591484f2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5126821370750135"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Difference-in-difference (before)\n",
    "mean_treatment_before - mean_control_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dKWxh9S2DQ4n",
    "outputId": "83a2b6e1-7798-4767-97fa-b9b4b0922dc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2494172494172493"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Difference-in-difference (after)\n",
    "mean_treatment_after - mean_control_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAJqs9yKEIoj"
   },
   "source": [
    "**Difference-in-Difference Using Fit OLS Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eXCjX3CgEH19"
   },
   "outputs": [],
   "source": [
    "def fit_OLS_model(df, target_variable, explanatory_variables, intercept = False):\n",
    "    \"\"\"\n",
    "    Fits an OLS model from data.\n",
    "    \n",
    "    Inputs:\n",
    "        df: pandas DataFrame\n",
    "        target_variable: string, name of the target variable\n",
    "        explanatory_variables: list of strings, names of the explanatory variables\n",
    "        intercept: bool, if True add intercept term\n",
    "    Outputs:\n",
    "        fitted_model: model containing OLS regression results\n",
    "    \"\"\"\n",
    "    \n",
    "    target = df[target_variable]\n",
    "    inputs = df[explanatory_variables]\n",
    "    if intercept:\n",
    "        inputs = sm.add_constant(inputs)\n",
    "    \n",
    "    fitted_model = sm.OLS(target, inputs).fit()\n",
    "    return(fitted_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jFm84PG4DZLT",
    "outputId": "b0ae7c72-fb00-4833-c97e-773fa7c24df1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    }
   ],
   "source": [
    "linear_model = fit_OLS_model(did_df, 'y', ['T', 'I', 'T*I', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age'], intercept=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R-NyDIbuFbFn",
    "outputId": "e6d022e8-908b-4942-a266-997ad3aae246"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.094\n",
      "Model:                            OLS   Adj. R-squared:                  0.086\n",
      "Method:                 Least Squares   F-statistic:                     11.94\n",
      "Date:                Tue, 03 May 2022   Prob (F-statistic):           3.08e-09\n",
      "Time:                        04:04:04   Log-Likelihood:                -446.12\n",
      "No. Observations:                 465   AIC:                             902.2\n",
      "Df Residuals:                     460   BIC:                             923.0\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "const                  -46.1205     24.137     -1.911      0.057     -93.553       1.312\n",
      "T                       63.3450     33.152      1.911      0.057      -1.803     128.493\n",
      "I                        0.1763      0.065      2.718      0.007       0.049       0.304\n",
      "T*I                     63.3450     33.152      1.911      0.057      -1.803     128.493\n",
      "median_income           -0.0024      0.001     -1.893      0.059      -0.005    9.21e-05\n",
      "pop_density              0.1088      0.057      1.913      0.056      -0.003       0.221\n",
      "unemployment            -5.6195      2.941     -1.911      0.057     -11.399       0.160\n",
      "mean_participant_age    -0.0016      0.003     -0.569      0.570      -0.007       0.004\n",
      "==============================================================================\n",
      "Omnibus:                      134.413   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              425.322\n",
      "Skew:                           1.334   Prob(JB):                     4.39e-93\n",
      "Kurtosis:                       6.851   Cond. No.                     3.25e+21\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 6.44e-32. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "print(linear_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qlO1Ff5fHksY"
   },
   "source": [
    "# Analysis\n",
    "Observing the Fit OLS model generated based on our target variable \"Y\" (the sum of Gun Violence injuries and deaths) against our treatment variable (Act of 192) and confounding variables (median income, population density, unemployment, and mean of participant's age) the constant coefficient demonstrated negative number of -46.1205, which signifies negative relationship between our target variable against our confounding variables before and after intervention of Act of 192. This also signifies that the general number of injuries and deaths from gun violence incidents have gone down over time, which was also demonstrated from our previous analysis that showed decreasing number of gun violence cases over time due to increasing number of gun control federal state laws that were instated throughout the entire nation. \n",
    "\n",
    "As indicated from the summary of the fit OLS model, standard errors are fairly high for our target variable which might not entire represent our population. The standard error was 24.137 for our constant. \n",
    "\n",
    "In our conclusion, given the high error rate from our OLS model, we discovered unexpected founding on the correlation between treatment variable of Act of 192 intervention and our target variable on the overall number of injuries and deaths from gun violence cases. Therefore, we came to conclusion that it is hard to demonstrate causality between our treatment and target variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IFBJLBBhFluU"
   },
   "outputs": [],
   "source": [
    "#explain the coefficients of confounding variables and the impacts. \n",
    "#add part about errors and uncertainty.\n",
    "#indicate that standard error was very high."
   ]
  },
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