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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 DoWhy
Step1:  Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. 
Beta is the true causal effect.
Step2:  Note that we are using a pandas dataframe to load the data.
Identifying the causal estimand
We now input a causal graph in the DOT graph format.
Step3:  We get a causal graph. Now identification and estimation is done.
Step4:  Method 1
Step5:  Method 2
Step6:  Method 3
Step7:  Method 4
Step8:  Method 5
Step9:  Method 6
Step10:  Method 7
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Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import logging
import dowhy
from dowhy import CausalModel
import dowhy.datasets 
Explanation: DoWhy: Different estimation methods for causal inference
This is a quick introduction to the DoWhy causal inference library.
We will load in a sample dataset and use different methods for estimating the causal effect of a (pre-specified)treatment variable on a (pre-specified) outcome variable.
We will see that not all estimators return the correct effect for this dataset.
First, let us add the required path for Python to find the DoWhy code and load all required packages
End of explanation
data = dowhy.datasets.linear_dataset(beta=10,
        num_common_causes=5, 
        num_instruments = 2,
        num_treatments=1,
        num_samples=10000,
        treatment_is_binary=True,
        outcome_is_binary=False,
        stddev_treatment_noise=10)
df = data["df"]
df
Explanation: Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. 
Beta is the true causal effect.
End of explanation
# With graph
model=CausalModel(
        data = df,
        treatment=data["treatment_name"],
        outcome=data["outcome_name"],
        graph=data["gml_graph"],
        instruments=data["instrument_names"]
        )
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))
Explanation: Note that we are using a pandas dataframe to load the data.
Identifying the causal estimand
We now input a causal graph in the DOT graph format.
End of explanation
identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
print(identified_estimand)
Explanation: We get a causal graph. Now identification and estimation is done.
End of explanation
causal_estimate_reg = model.estimate_effect(identified_estimand,
        method_name="backdoor.linear_regression",
        test_significance=True)
print(causal_estimate_reg)
print("Causal Estimate is " + str(causal_estimate_reg.value))
Explanation: Method 1: Regression
Use linear regression.
End of explanation
causal_estimate_dmatch = model.estimate_effect(identified_estimand,
                                              method_name="backdoor.distance_matching",
                                              target_units="att",
                                              method_params={'distance_metric':"minkowski", 'p':2})
print(causal_estimate_dmatch)
print("Causal Estimate is " + str(causal_estimate_dmatch.value))
Explanation: Method 2: Distance Matching
Define a distance metric and then use the metric to match closest points between treatment and control.
End of explanation
causal_estimate_strat = model.estimate_effect(identified_estimand,
                                              method_name="backdoor.propensity_score_stratification",
                                              target_units="att")
print(causal_estimate_strat)
print("Causal Estimate is " + str(causal_estimate_strat.value))
Explanation: Method 3: Propensity Score Stratification
We will be using propensity scores to stratify units in the data.
End of explanation
causal_estimate_match = model.estimate_effect(identified_estimand,
                                              method_name="backdoor.propensity_score_matching",
                                              target_units="atc")
print(causal_estimate_match)
print("Causal Estimate is " + str(causal_estimate_match.value))
Explanation: Method 4: Propensity Score Matching
We will be using propensity scores to match units in the data.
End of explanation
causal_estimate_ipw = model.estimate_effect(identified_estimand,
                                            method_name="backdoor.propensity_score_weighting",
                                            target_units = "ate",
                                            method_params={"weighting_scheme":"ips_weight"})
print(causal_estimate_ipw)
print("Causal Estimate is " + str(causal_estimate_ipw.value))
Explanation: Method 5: Weighting
We will be using (inverse) propensity scores to assign weights to units in the data. DoWhy supports a few different weighting schemes:
1. Vanilla Inverse Propensity Score weighting (IPS) (weighting_scheme="ips_weight")
2. Self-normalized IPS weighting (also known as the Hajek estimator) (weighting_scheme="ips_normalized_weight")
3. Stabilized IPS weighting (weighting_scheme = "ips_stabilized_weight")
End of explanation
causal_estimate_iv = model.estimate_effect(identified_estimand,
        method_name="iv.instrumental_variable", method_params = {'iv_instrument_name': 'Z0'})
print(causal_estimate_iv)
print("Causal Estimate is " + str(causal_estimate_iv.value))
Explanation: Method 6: Instrumental Variable
We will be using the Wald estimator for the provided instrumental variable.
End of explanation
causal_estimate_regdist = model.estimate_effect(identified_estimand,
        method_name="iv.regression_discontinuity", 
        method_params={'rd_variable_name':'Z1',
                       'rd_threshold_value':0.5,
                       'rd_bandwidth': 0.15})
print(causal_estimate_regdist)
print("Causal Estimate is " + str(causal_estimate_regdist.value))
Explanation: Method 7: Regression Discontinuity
We will be internally converting this to an equivalent instrumental variables problem.
End of explanation
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