Demo for the DoWhy causal API

We show a simple example of adding a causal extension to any dataframe.

import dowhy.datasets
import dowhy.api
from dowhy.graph import build_graph_from_str

import numpy as np
import pandas as pd

from statsmodels.api import OLS

data = dowhy.datasets.linear_dataset(beta=5,
        num_common_causes=1,
        num_instruments = 0,
        num_samples=1000,
        treatment_is_binary=True)
df = data['df']
df['y'] = df['y'] + np.random.normal(size=len(df)) # Adding noise to data. Without noise, the variance in Y|X, Z is zero, and mcmc fails.
nx_graph = build_graph_from_str(data["dot_graph"])

treatment= data["treatment_name"][0]
outcome = data["outcome_name"][0]
common_cause = data["common_causes_names"][0]
df

	W0	v0	y
0	-0.350584	True	2.693066
1	2.343845	True	8.823433
2	-0.853574	False	-0.760625
3	-0.720307	True	0.992088
4	-1.767053	False	-3.543647
...	...	...	...
995	0.288447	True	2.620491
996	1.189487	False	1.842660
997	-1.345706	True	3.080546
998	-0.989674	True	4.430537
999	-2.001564	False	-5.360771

1000 rows × 3 columns

# data['df'] is just a regular pandas.DataFrame
df.causal.do(x=treatment,
             variable_types={treatment: 'b', outcome: 'c', common_cause: 'c'},
             outcome=outcome,
             common_causes=[common_cause],
            ).groupby(treatment).mean().plot(y=outcome, kind='bar')

<Axes: xlabel='v0'>

df.causal.do(x={treatment: 1},
              variable_types={treatment:'b', outcome: 'c', common_cause: 'c'},
              outcome=outcome,
              method='weighting',
              common_causes=[common_cause]
              ).groupby(treatment).mean().plot(y=outcome, kind='bar')

<Axes: xlabel='v0'>

cdf_1 = df.causal.do(x={treatment: 1},
              variable_types={treatment: 'b', outcome: 'c', common_cause: 'c'},
              outcome=outcome,
              graph=nx_graph
              )

cdf_0 = df.causal.do(x={treatment: 0},
              variable_types={treatment: 'b', outcome: 'c', common_cause: 'c'},
              outcome=outcome,
              graph=nx_graph
              )

cdf_0

	W0	v0	y	propensity_score	weight
0	-1.799174	False	-3.835996	0.564483	1.771534
1	-3.050651	False	-4.819457	0.607009	1.647421
2	-1.641033	False	-3.679600	0.559027	1.788824
3	-1.049986	False	-1.169226	0.538519	1.856944
4	-1.574879	False	-1.591004	0.556740	1.796171
...	...	...	...	...	...
995	-0.424351	False	0.179638	0.516670	1.935471
996	-0.615770	False	-2.113757	0.523366	1.910710
997	-0.728079	False	-1.554656	0.527290	1.896488
998	0.183197	False	2.920524	0.495391	2.018608
999	0.222296	False	1.569353	0.494021	2.024205

1000 rows × 5 columns

cdf_1

	W0	v0	y	propensity_score	weight
0	-0.309410	True	5.237502	0.487353	2.051899
1	-2.441848	True	1.075175	0.413518	2.418272
2	-0.076482	True	4.246861	0.495512	2.018116
3	-1.191165	True	2.585076	0.456568	2.190255
4	-0.321887	True	4.129084	0.486917	2.053740
...	...	...	...	...	...
995	0.256963	True	4.418986	0.507193	1.971635
996	-2.834070	True	-0.123419	0.400254	2.498412
997	0.866529	True	7.324587	0.528519	1.892079
998	-1.271193	True	1.413350	0.453787	2.203679
999	-1.498723	True	3.464655	0.445895	2.242678

1000 rows × 5 columns

Comparing the estimate to Linear Regression

First, estimating the effect using the causal data frame, and the 95% confidence interval.

(cdf_1['y'] - cdf_0['y']).mean()

$\displaystyle 4.86500119177374$

1.96*(cdf_1['y'] - cdf_0['y']).std() / np.sqrt(len(df))

$\displaystyle 0.190519413611057$

Comparing to the estimate from OLS.

model = OLS(np.asarray(df[outcome]), np.asarray(df[[common_cause, treatment]], dtype=np.float64))
result = model.fit()
result.summary()

OLS Regression Results

Dep. Variable:	y	R-squared (uncentered):	0.916
Model:	OLS	Adj. R-squared (uncentered):	0.916
Method:	Least Squares	F-statistic:	5470.
Date:	Wed, 19 Mar 2025	Prob (F-statistic):	0.00
Time:	15:19:10	Log-Likelihood:	-1442.5
No. Observations:	1000	AIC:	2889.
Df Residuals:	998	BIC:	2899.
Df Model:	2
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
x1	1.8471	0.028	65.017	0.000	1.791	1.903
x2	4.8847	0.050	98.475	0.000	4.787	4.982

Omnibus:	2.561	Durbin-Watson:	2.009
Prob(Omnibus):	0.278	Jarque-Bera (JB):	2.367
Skew:	-0.052	Prob(JB):	0.306
Kurtosis:	2.786	Cond. No.	1.93

Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.