# Confounding Example: Finding causal effects from observed data¶

Suppose you are given some data with treatment and outcome. Can you determine whether the treatment causes the outcome, or is the correlation purely due to another common cause?

In [1]:

import os, sys
sys.path.append(os.path.abspath("../../"))

In [2]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import dowhy
from dowhy.do_why import CausalModel
import dowhy.datasets, dowhy.plotter


# Let’s create a mystery dataset. Need to find if there is a causal effect.¶

Creating the dataset. It is generated from either of two models: * Model 1: Treatment does cause outcome. * Model 2: Treatment does not cause outcome. All observed correlation is due to a common cause.

In [3]:

rvar = 1 if np.random.uniform() >0.5 else 0
data_dict = dowhy.datasets.xy_dataset(10000, effect=rvar, sd_error=0.2)
df = data_dict['df']

   Treatment    Outcome        w0
0   2.964978   5.858518 -3.173399
1   3.696709   7.945649 -1.936995
2   2.125228   4.076005 -3.975566
3   6.635687  13.471594  0.772480
4   9.600072  19.577649  3.922406

In [4]:

dowhy.plotter.plot_treatment_outcome(df[data_dict["treatment_name"]], df[data_dict["outcome_name"]],
df[data_dict["time_val"]])


# Using DoWhy to resolve the mystery: Does Treatment cause Outcome variable?¶

## STEP 1: Model the problem as a causal graph¶

Initializing the causal model.

In [5]:

model= CausalModel(
data=df,
treatment=data_dict["treatment_name"],
outcome=data_dict["outcome_name"],
common_causes=data_dict["common_causes_names"],
instruments=data_dict["instrument_names"])
model.view_model(layout="dot")

WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.

Model to find the causal effect of treatment Treatment on outcome Outcome


Showing the causal model stored in local file “causal_model.png”

In [6]:

from IPython.display import Image, display
display(Image(filename="causal_model.png"))


## STEP 2: Identify causal effect using properties of the formal causal graph¶

Identify the causal effect using properties of the causal graph.

In [7]:

identified_estimand = model.identify_effect()
print(identified_estimand)

INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'w0', 'U'}

{'observed': 'yes'}
{'observed': 'no', 'label': 'Unobserved Confounders'}
There are unobserved common causes. Causal effect cannot be identified.
WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] y

INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]

Estimand type: ate
### Estimand : 1
Estimand name: iv
No such variable found!
### Estimand : 2
Estimand name: backdoor
Estimand expression:
d
──────────(Expectation(Outcome|w0))
dTreatment
Estimand assumption 1, Unconfoundedness: If U→Treatment and U→Outcome then P(Outcome|Treatment,w0,U) = P(Outcome|Treatment,w0)



## STEP 3: Estimate the causal effect¶

Once we have the identified estimand, can use any statistical method to estimate the causal effect.

Let’s use Linear Regression for simplicity.

In [8]:

estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.linear_regression")
print("Causal Estimate is " + str(estimate.value))

# Plot Slope of line between treamtent and outcome =causal effect
dowhy.plotter.plot_causal_effect(estimate, df[data_dict["treatment_name"]], df[data_dict["outcome_name"]])

INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator
INFO:dowhy.causal_estimator:b: Outcome~Treatment+w0

LinearRegressionEstimator
Causal Estimate is 0.0180444904797


### Checking if the estimate is correct¶

In [9]:

print("DoWhy estimate is " + str(estimate.value))
print ("Actual true causal effect was {0}".format(rvar))

DoWhy estimate is 0.0180444904797
Actual true causal effect was 0


## Step 4: Refuting the estimate¶

We can also refute the estimate to check its robustness to assumptions (aka sensitivity analysis, but on steroids).

### Adding a random common cause variable¶

In [10]:

res_random=model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause")
print(res_random)

INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator
INFO:dowhy.causal_estimator:b: Outcome~Treatment+w0+w_random

Refute: Add a Random Common Cause
Estimated effect:(0.018044490479725939,)
New effect:(0.017899599388015757,)



### Replacing treatment with a random (placebo) variable¶

In [11]:

res_placebo=model.refute_estimate(identified_estimand, estimate,
method_name="placebo_treatment_refuter", placebo_type="permute")
print(res_placebo)

INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator
INFO:dowhy.causal_estimator:b: Outcome~placebo+w0

Refute: Use a Placebo Treatment
Estimated effect:(0.018044490479725939,)
New effect:(0.00017882059335885532,)



### Removing a random subset of the data¶

In [12]:

res_subset=model.refute_estimate(identified_estimand, estimate,
method_name="data_subset_refuter", subset_fraction=0.9)
print(res_subset)


INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator
INFO:dowhy.causal_estimator:b: Outcome~Treatment+w0

Refute: Use a subset of data
Estimated effect:(0.018044490479725939,)
New effect:(0.018649516996423924,)



As you can see, our causal estimator is robust to simple refutations.