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	0.319949	True	5.882584
1	0.440958	True	4.622104
2	-0.880456	False	0.734731
3	1.352040	False	2.243402
4	-0.119246	True	5.176788
...	...	...	...
995	0.223470	False	1.116045
996	0.450676	True	6.721430
997	0.814874	True	4.027162
998	1.359336	True	7.286949
999	-0.515406	False	0.779886

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	0.259164	False	-1.025702	0.499463	2.002150
1	0.330791	False	1.201951	0.492570	2.030169
2	0.289888	False	0.844696	0.496506	2.014074
3	1.543579	False	0.812667	0.378333	2.643175
4	0.037070	False	0.141225	0.520827	1.920024
...	...	...	...	...	...
995	-0.448824	False	-0.357686	0.567196	1.763060
996	0.934334	False	2.766019	0.434853	2.299629
997	-0.348975	False	0.473715	0.557736	1.792964
998	0.821882	False	0.798649	0.445521	2.244566
999	0.287035	False	0.391905	0.496781	2.012961

1000 rows × 5 columns

cdf_1

	W0	v0	y	propensity_score	weight
0	3.516035	True	9.864709	0.778337	1.284791
1	-0.783727	True	3.441380	0.401468	2.490858
2	-0.559160	True	1.803474	0.422408	2.367380
3	-0.010100	True	4.910736	0.474643	2.106847
4	1.094514	True	4.930522	0.580237	1.723434
...	...	...	...	...	...
995	1.607544	True	5.726897	0.627441	1.593774
996	0.237660	True	5.249902	0.498467	2.006150
997	-0.226316	True	4.621072	0.453942	2.202925
998	-1.045048	True	4.631207	0.377551	2.648650
999	-0.789050	True	5.500858	0.400976	2.493916

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()

$\displaystyle 4.80317963672906$

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

$\displaystyle 0.136134908742183$

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):	0.947
Model:	OLS	Adj. R-squared (uncentered):	0.947
Method:	Least Squares	F-statistic:	8904.
Date:	Thu, 12 Feb 2026	Prob (F-statistic):	0.00
Time:	23:22:09	Log-Likelihood:	-1459.8
No. Observations:	1000	AIC:	2924.
Df Residuals:	998	BIC:	2933.
Df Model:	2
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
x1	1.0730	0.031	34.403	0.000	1.012	1.134
x2	4.9654	0.052	96.222	0.000	4.864	5.067

Omnibus:	0.793	Durbin-Watson:	2.074
Prob(Omnibus):	0.673	Jarque-Bera (JB):	0.684
Skew:	-0.054	Prob(JB):	0.711
Kurtosis:	3.068	Cond. No.	2.11

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.