examples/TotalEffect.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and settings\n",
    "In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`."
   ]
  },
  {
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     "end_time": "2020-02-20T02:15:24.591144Z",
     "start_time": "2020-02-20T02:15:20.359835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1.24.4', '2.0.3', '0.20.1', '1.8.3']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import graphviz\n",
    "import lingam\n",
    "from lingam.utils import make_dot\n",
    "\n",
    "print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])\n",
    "\n",
    "np.set_printoptions(precision=3, suppress=True)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data\n",
    "We create test data consisting of 6 variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T02:15:24.707612Z",
     "start_time": "2020-02-20T02:15:24.620349Z"
    }
   },
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>15.780259</td>\n",
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    "x3 = np.random.uniform(size=10000)\n",
    "x0 = 3.0*x3 + np.random.uniform(size=10000)\n",
    "x2 = 6.0*x3 + np.random.uniform(size=10000)\n",
    "x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=10000)\n",
    "x5 = 4.0*x0 + np.random.uniform(size=10000)\n",
    "x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=10000)\n",
    "X = pd.DataFrame(np.array([x0, x1, x2, x3, x4, x5]).T ,columns=['x0', 'x1', 'x2', 'x3', 'x4', 'x5'])\n",
    "X.head()"
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    "m = np.array([[0.0, 0.0, 0.0, 3.0, 0.0, 0.0],\n",
    "              [3.0, 0.0, 2.0, 0.0, 0.0, 0.0],\n",
    "              [0.0, 0.0, 0.0, 6.0, 0.0, 0.0],\n",
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal Discovery\n",
    "To run causal discovery, we create a `DirectLiNGAM` object and call the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "model = lingam.DirectLiNGAM()\n",
    "model.fit(X)\n",
    "make_dot(model.adjacency_matrix_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Effect\n",
    "To estimate the total effect, we can call `estimate_total_effect` method with lingam model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T02:15:27.171700Z",
     "start_time": "2020-02-20T02:15:27.148305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total effect: 21.002\n"
     ]
    }
   ],
   "source": [
    "# x3 --> x1\n",
    "te = model.estimate_total_effect(X, 3, 1)\n",
    "print(f'total effect: {te:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T02:15:27.201121Z",
     "start_time": "2020-02-20T02:15:27.178989Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total effect: 2.007\n"
     ]
    }
   ],
   "source": [
    "# x2 --> x1\n",
    "te = model.estimate_total_effect(X, 2, 1)\n",
    "print('total effect: {:.3f}'.format(te))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T02:15:27.221299Z",
     "start_time": "2020-02-20T02:15:27.205103Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total effect: 0.000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/Foo/.local/lib/python3.8/site-packages/lingam/base.py:75: UserWarning: The estimated causal effect may be incorrect because the causal order of the destination variable (to_index=3) is earlier than the source variable (from_index=1).\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# x1 --> x3 (Warning!)\n",
    "# The causal order of the destination variable is earlier than the source variable.\n",
    "te = model.estimate_total_effect(X, 1, 3)\n",
    "print('total effect: {:.3f}'.format(te))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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