def dowhy_quick_backdoor_estimator(dataframe,
outcome,
treatment,
cofounders_list,
method_name,
populaton_of_interest='ate',
view_model=False):
"""
Make a quick statistical assessment for the mean of 2 different samples (hypothesis test based)
:param dataframe: original dataframe in a subject level
:param group_col: the name of the group column
:param category_col: the name of the category_col column
:returns group_share_per_category_df: df containing the % share each category has by group
"""
causal_model = CausalModel(data=dataframe,
treatment=treatment,
outcome=outcome,
common_causes=cofounders_list)
if view_model:
causal_model.view_model(layout="dot")
identified_estimand = causal_model.identify_effect(
proceed_when_unidentifiable=True)
causal_estimate = causal_model.estimate_effect(
identified_estimand,
method_name=method_name,
target_units=
populaton_of_interest #, confidence_intervals=True # not in this release
)
return causal_estimate.value
def att_causal_estimator(df,
outcome,
treatment,
cofounders_list,
method_name,
view_model=False):
causal_model = CausalModel(data=df,
treatment=treatment,
outcome=outcome,
common_causes=cofounders_list)
if view_model:
causal_model.view_model(layout="dot")
identified_estimand = causal_model.identify_effect(
proceed_when_unidentifiable=True)
causal_estimate = causal_model.estimate_effect(
identified_estimand,
method_name=method_name,
target_units='att', #, confidence_intervals=True
)
return (causal_estimate.value)
from datasets import *
from dowhy import CausalModel
credit_data = get_credit()
model = CausalModel(
data=credit_data["df"],
treatment=["YearsEmployed"],
outcome=["Approved"],
graph=credit_data["dot_graph"],
)
from sklearn.linear_model import LogisticRegressionCV
# Saves the model as "causal_model.png"
model.view_model(layout="dot")
identified_estimand_binary = model.identify_effect(
proceed_when_unidentifiable=True)
# estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.drlearner.LinearDRLearner")
orthoforest_estimate = model.estimate_effect(
identified_estimand_binary,
method_name="backdoor.econml.ortho_forest.ContinuousTreatmentOrthoForest",
target_units=lambda df: df["Male"] == 1,
confidence_intervals=False,
method_params={
"init_params": {
'n_trees':
2, # not ideal, just as an example to speed up computation
},
"fit_params": {}
data = data.astype({"treatment": 'bool'}, copy=False)
print(data.head())
# Create a causal model from the data and given common causes.
xs = ""
for i in range(1, 26):
xs += ("x" + str(i) + "+")
model = CausalModel(
data=data,
treatment='treatment',
outcome='y_factual',
common_causes=xs.split('+'),
)
#save the model as a png
model.view_model()
display(Image(filename="causal_model.png"))
#Identify the causal effect
identified_estimand = model.identify_effect()
print(identified_estimand)
# Estimate the causal effect and compare it with Average Treatment Effect
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.linear_regression",
test_significance=True)
print(estimate)
print("Causal Estimate is " + str(estimate.value))
data_1 = data[data["treatment"] == 1]