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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DoWhy: Doubly robust estimator for causal effect estimation\n",
+    "\n",
+    "This notebook shows how the doubly robust estimator in doWhy can be used for causal effect estimation.\n",
+    "\n",
+    "The doubly robust estimator is a powerful technique in causal inference that combines the strengths of both outcome regression and propensity score methods to provide more accurate and reliable estimates of causal effects. By leveraging models for both the treatment assignment and the outcome, this method remains consistent if at least one of these models is correctly specified, offering robustness against model misspecification. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0. Import required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import dowhy\n",
+    "from dowhy import CausalModel\n",
+    "import dowhy.datasets \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Load data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we'll be using the [Learning mindset dataset from Athey et al.](https://arxiv.org/pdf/1902.07409), which explores the impact of a growth mindset intervention on students' academic performance. The dataset includes information on various student characteristics, such as demographics and baseline academic performance, as well as details about the intervention and its outcomes. By analyzing this dataset, we aim to estimate the causal effect of the mindset intervention on students' subsequent grades. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"./learning_mindset.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>schoolid</th>\n",
+       "      <th>intervention</th>\n",
+       "      <th>achievement_score</th>\n",
+       "      <th>success_expect</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>frst_in_family</th>\n",
+       "      <th>school_urbanicity</th>\n",
+       "      <th>school_mindset</th>\n",
+       "      <th>school_achievement</th>\n",
+       "      <th>school_ethnic_minority</th>\n",
+       "      <th>school_poverty</th>\n",
+       "      <th>school_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.277359</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.449646</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.769703</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.121763</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.526147</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   schoolid  intervention  achievement_score  success_expect  ethnicity  \\\n",
+       "0        76             1           0.277359               6          4   \n",
+       "1        76             1          -0.449646               4         12   \n",
+       "2        76             1           0.769703               6          4   \n",
+       "3        76             1          -0.121763               6          4   \n",
+       "4        76             1           1.526147               6          4   \n",
+       "\n",
+       "   gender  frst_in_family  school_urbanicity  school_mindset  \\\n",
+       "0       2               1                  4        0.334544   \n",
+       "1       2               1                  4        0.334544   \n",
+       "2       2               0                  4        0.334544   \n",
+       "3       2               0                  4        0.334544   \n",
+       "4       1               0                  4        0.334544   \n",
+       "\n",
+       "   school_achievement  school_ethnic_minority  school_poverty  school_size  \n",
+       "0            0.648586               -1.310927        0.224077    -0.426757  \n",
+       "1            0.648586               -1.310927        0.224077    -0.426757  \n",
+       "2            0.648586               -1.310927        0.224077    -0.426757  \n",
+       "3            0.648586               -1.310927        0.224077    -0.426757  \n",
+       "4            0.648586               -1.310927        0.224077    -0.426757  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we cleanup unncessary columns from our dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10391, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "categorical = [\"ethnicity\", \"gender\", \"school_urbanicity\"]\n",
+    "continuous = [\"school_mindset\", \"school_achievement\", \"school_ethnic_minority\", \"school_poverty\", \"school_size\"]\n",
+    "\n",
+    "cleaned_data = pd.concat([\n",
+    "    data.drop(columns=categorical),\n",
+    "    pd.get_dummies(data[categorical], columns=categorical, drop_first=False) \n",
+    "], axis=1)\n",
+    "\n",
+    "print(cleaned_data.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Create a causal graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we define our assumptions in the form fo a causal gprah. This graph represents the relationships between the variables in our dataset and encodes our prior knowledge about the causal mechanisms at play."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "causal_graph = \"\"\"\n",
+    "    digraph causal_graph {\n",
+    "    schoolid;\n",
+    "    intervention;\n",
+    "    achievement_score;\n",
+    "    success_expect;\n",
+    "    ethnicity;\n",
+    "    gender;\n",
+    "    frst_in_family;\n",
+    "    school_urbanicity;\n",
+    "    school_mindset;\n",
+    "    school_achievement;\n",
+    "    school_ethnic_minority;\n",
+    "    school_poverty;\n",
+    "    school_size;\n",
+    "    intervention -> achievement_score;\n",
+    "    intervention -> success_expect;\n",
+    "    ethnicity -> achievement_score;\n",
+    "    gender -> achievement_score;\n",
+    "    frst_in_family -> achievement_score;\n",
+    "    school_urbanicity -> achievement_score;\n",
+    "    school_mindset -> achievement_score;\n",
+    "    school_achievement -> achievement_score;\n",
+    "    school_ethnic_minority -> achievement_score;\n",
+    "    school_poverty -> achievement_score;\n",
+    "    school_size -> achievement_score;\n",
+    "    school_mindset -> school_achievement;\n",
+    "}\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model= dowhy.CausalModel(\n",
+    "        data = cleaned_data,\n",
+    "        graph=causal_graph.replace(\"\\n\", \" \"),\n",
+    "        treatment=\"intervention\",\n",
+    "        outcome=\"achievement_score\")\n",
+    "\n",
+    "model.view_model()\n",
+    "\n",
+    "from IPython.display import Image, display\n",
+    "display(Image(filename=\"causal_model.png\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Identify effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimand type: nonparametric-ate\n",
+      "\n",
+      "### Estimand : 1\n",
+      "Estimand name: backdoor\n",
+      "Estimand expression:\n",
+      "       d                             \n",
+      "───────────────(E[achievement_score])\n",
+      "d[intervention]                      \n",
+      "Estimand assumption 1, Unconfoundedness: If U→{intervention} and U→achievement_score then P(achievement_score|intervention,,U) = P(achievement_score|intervention,)\n",
+      "\n",
+      "### Estimand : 2\n",
+      "Estimand name: iv\n",
+      "No such variable(s) found!\n",
+      "\n",
+      "### Estimand : 3\n",
+      "Estimand name: frontdoor\n",
+      "No such variable(s) found!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
+    "print(identified_estimand)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Estimate the identified estimand with the doubly robust estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m estimate \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_effect\u001b[49m\u001b[43m(\u001b[49m\u001b[43midentified_estimand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdoubly_robust_estimator\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/homebrew/anaconda3/envs/dowhy/lib/python3.11/site-packages/dowhy/causal_model.py:257\u001b[0m, in \u001b[0;36mCausalModel.estimate_effect\u001b[0;34m(self, identified_estimand, method_name, control_value, treatment_value, test_significance, evaluate_effect_strength, confidence_intervals, target_units, effect_modifiers, fit_estimator, method_params)\u001b[0m\n\u001b[1;32m    255\u001b[0m str_arr \u001b[38;5;241m=\u001b[39m method_name\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m, maxsplit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    256\u001b[0m identifier_name \u001b[38;5;241m=\u001b[39m str_arr[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 257\u001b[0m estimator_name \u001b[38;5;241m=\u001b[39m \u001b[43mstr_arr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    258\u001b[0m identified_estimand\u001b[38;5;241m.\u001b[39mset_identifier_method(identifier_name)\n\u001b[1;32m    259\u001b[0m \u001b[38;5;66;03m# This is done as all dowhy estimators have two parts and external ones have two or more parts\u001b[39;00m\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "estimate = model.estimate_effect(identified_estimand, \n",
+    "                                 method_name=\"doubly_robust_estimator\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Refute results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TODO: Write description on refuting results from doubly robust estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'estimate' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m refute1_results\u001b[38;5;241m=\u001b[39mmodel\u001b[38;5;241m.\u001b[39mrefute_estimate(identified_estimand, \u001b[43mestimate\u001b[49m,\n\u001b[1;32m      2\u001b[0m         method_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_common_cause\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(refute1_results)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'estimate' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "refute1_results=model.refute_estimate(identified_estimand, estimate,\n",
+    "        method_name=\"random_common_cause\")\n",
+    "print(refute1_results)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dowhy",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/example_notebooks/learning_mindset.csv b/docs/source/example_notebooks/learning_mindset.csv
new file mode 100644
index 0000000000..a975b2d6a1
--- /dev/null
+++ b/docs/source/example_notebooks/learning_mindset.csv
@@ -0,0 +1,10392 @@
+schoolid,intervention,achievement_score,success_expect,ethnicity,gender,frst_in_family,school_urbanicity,school_mindset,school_achievement,school_ethnic_minority,school_poverty,school_size
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\ No newline at end of file
diff --git a/dowhy/causal_estimators/doubly_robust_estimator.py b/dowhy/causal_estimators/doubly_robust_estimator.py
new file mode 100644
index 0000000000..020565f333
--- /dev/null
+++ b/dowhy/causal_estimators/doubly_robust_estimator.py
@@ -0,0 +1,162 @@
+import copy
+from typing import Any, List, Optional, Type, Union
+
+import numpy as np
+import pandas as pd
+
+from dowhy.causal_estimator import CausalEstimate, CausalEstimator
+from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
+from dowhy.causal_estimators.propensity_score_weighting_estimator import PropensityScoreWeightingEstimator
+from dowhy.causal_identifier import EstimandType, IdentifiedEstimand
+from dowhy.utils.api import parse_state
+
+
+class DoublyRobustEstimator(CausalEstimator):
+    """Compute treatment effect whenever the effect is fully mediated by
+    another variable (front-door) or when there is an instrument available.
+
+    Currently only supports a linear model for the effects.
+
+    Supports additional parameters as listed below.
+
+    """
+
+    # First stage statistical model
+    DEFAULT_FIRST_STAGE_MODEL = LinearRegressionEstimator
+
+    # Second stage statistical model
+    DEFAULT_SECOND_STAGE_MODEL = PropensityScoreWeightingEstimator
+
+    def __init__(
+        self,
+        identified_estimand: IdentifiedEstimand,
+        test_significance: Union[bool, str] = False,
+        evaluate_effect_strength: bool = False,
+        confidence_intervals: bool = False,
+        num_null_simulations: int = CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST,
+        num_simulations: int = CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_CI,
+        sample_size_fraction: int = CausalEstimator.DEFAULT_SAMPLE_SIZE_FRACTION,
+        confidence_level: float = CausalEstimator.DEFAULT_CONFIDENCE_LEVEL,
+        need_conditional_estimates: Union[bool, str] = "auto",
+        num_quantiles_to_discretize_cont_cols: int = CausalEstimator.NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS,
+        first_stage_model: Optional[Union[CausalEstimator, Type[CausalEstimator]]] = None,
+        second_stage_model: Optional[Union[CausalEstimator, Type[CausalEstimator]]] = None,
+        **kwargs,
+    ):
+        """
+        :param identified_estimand: probability expression
+            representing the target identified estimand to estimate.
+        :param test_significance: Binary flag or a string indicating whether to test significance and by which method. All estimators support test_significance="bootstrap" that estimates a p-value for the obtained estimate using the bootstrap method. Individual estimators can override this to support custom testing methods. The bootstrap method supports an optional parameter, num_null_simulations. If False, no testing is done. If True, significance of the estimate is tested using the custom method if available, otherwise by bootstrap.
+        :param evaluate_effect_strength: (Experimental) whether to evaluate the strength of effect
+        :param confidence_intervals: Binary flag or a string indicating whether the confidence intervals should be computed and which method should be used. All methods support estimation of confidence intervals using the bootstrap method by using the parameter confidence_intervals="bootstrap". The bootstrap method takes in two arguments (num_simulations and sample_size_fraction) that can be optionally specified in the params dictionary. Estimators may also override this to implement their own confidence interval method. If this parameter is False, no confidence intervals are computed. If True, confidence intervals are computed by the estimator's specific method if available, otherwise through bootstrap
+        :param num_null_simulations: The number of simulations for testing the
+            statistical significance of the estimator
+        :param num_simulations: The number of simulations for finding the
+            confidence interval (and/or standard error) for a estimate
+        :param sample_size_fraction: The size of the sample for the bootstrap
+            estimator
+        :param confidence_level: The confidence level of the confidence
+            interval estimate
+        :param need_conditional_estimates: Boolean flag indicating whether
+            conditional estimates should be computed. Defaults to True if
+            there are effect modifiers in the graph
+        :param num_quantiles_to_discretize_cont_cols: The number of quantiles
+            into which a numeric effect modifier is split, to enable
+            estimation of conditional treatment effect over it.
+        :param first_stage_model: First stage estimator to be used. Default is
+            linear regression.
+        :param second_stage_model: Second stage estimator to be used. Default
+            is linear regression.
+        :param kwargs: (optional) Additional estimator-specific parameters
+        """
+        super().__init__(
+            identified_estimand=identified_estimand,
+            test_significance=test_significance,
+            evaluate_effect_strength=evaluate_effect_strength,
+            confidence_intervals=confidence_intervals,
+            num_null_simulations=num_null_simulations,
+            num_simulations=num_simulations,
+            sample_size_fraction=sample_size_fraction,
+            confidence_level=confidence_level,
+            need_conditional_estimates=need_conditional_estimates,
+            num_quantiles_to_discretize_cont_cols=num_quantiles_to_discretize_cont_cols,
+            first_stage_model=first_stage_model,
+            second_stage_model=second_stage_model,
+            **kwargs,
+        )
+        self.logger.info("INFO: Using Doubly Robust Estimator")
+        # Check if the treatment is one-dimensional
+        if len(self._target_estimand.treatment_variable) > 1:
+            error_msg = str(self.__class__) + "cannot handle more than one treatment variable"
+            raise Exception(error_msg)
+        modified_target_estimand = copy.deepcopy(self._target_estimand)
+        modified_target_estimand.identifier_method = "backdoor"
+        modified_target_estimand.backdoor_variables = self._target_estimand.mediation_first_stage_confounders
+        if first_stage_model is not None:
+            self._first_stage_model = (
+                first_stage_model
+                if isinstance(first_stage_model, CausalEstimator)
+                else first_stage_model(
+                    modified_target_estimand,
+                    test_significance=self._significance_test,
+                    evaluate_effect_strength=self._effect_strength_eval,
+                    confidence_intervals=self._confidence_intervals,
+                    **kwargs,
+                )
+            )
+        else:
+            self._first_stage_model = self.__class__.DEFAULT_FIRST_STAGE_MODEL(
+                modified_target_estimand,
+                test_significance=self._significance_test,
+                evaluate_effect_strength=self._effect_strength_eval,
+                confidence_intervals=self._confidence_intervals,
+                **kwargs,
+            )
+            self.logger.warning("First stage model not provided. Defaulting to sklearn.linear_model.LinearRegression.")
+
+        modified_target_estimand = copy.deepcopy(self._target_estimand)
+       
+
+        pass
+
+    def estimate_effect(
+        self,
+        data: pd.DataFrame,
+        treatment_value: Any = 1,
+        control_value: Any = 0,
+        target_units=None,
+        **_,
+    ):
+        self._target_units = target_units
+        self._treatment_value = treatment_value
+        self._control_value = control_value
+
+        estimate_value = None
+        # First stage
+        first_stage_estimate = self._first_stage_model.estimate_effect(
+            data,
+            control_value=control_value,
+            treatment_value=treatment_value,
+            target_units=target_units,
+        )
+
+        # Second Stage
+        second_stage_estimate = self._second_stage_model.estimate_effect(
+            data,
+            control_value=control_value,
+            treatment_value=treatment_value,
+            target_units=target_units,
+        )
+
+        pass
+        #return estimate
+
+
+    def construct_symbolic_estimator(
+        self, first_stage_symbolic, second_stage_symbolic, total_effect_symbolic=None, estimand_type=None
+    ):
+        nie_symbolic = "(" + first_stage_symbolic + ")*(" + second_stage_symbolic + ")"
+        if estimand_type == EstimandType.NONPARAMETRIC_NIE:
+            return nie_symbolic
+        elif estimand_type == EstimandType.NONPARAMETRIC_NDE:
+            return "(" + total_effect_symbolic + ") - (" + nie_symbolic + ")"