# How to use the causality.estimation.parametric.InverseProbabilityWeightedLS function in causality

## To help you get started, we’ve selected a few causality examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. akelleh / causality / tests / unit / parametric.py View on Github ``````def test_estimators(self):
N = 2000
z = np.random.normal(size=N)
d = np.random.binomial(1, p=1. / (1. + np.exp(-z)))
y0 = np.random.normal(size=N)
y1 = y0 + 2. * (1 + z)
y = (d == 1) * y1 + (d == 0) * y0
X = pd.DataFrame({'d': d, 'z': z, 'y': y, 'y0': y0, 'y1': y1})

assignment = 'd'
confounder_types = {'z': 'c'}
outcome = 'y'
ipw_model = InverseProbabilityWeightedLS()
atc_lower, atc_exp, atc_upper = ipw_model.estimate_ATC(X,
assignment,
outcome,
confounder_types,
propensity_score_name='propensity score')
assert 0.9 * atc_lower &lt;= (X[X['d'] == 0]['y1'] - X[X['d'] == 0]['y0']).mean() &lt;= 1.1 * atc_upper

att_lower, att_exp, att_upper = ipw_model.estimate_ATT(X,
assignment,
outcome,
confounder_types,
propensity_score_name='propensity score')
assert 0.9 * att_lower &lt;= (X[X['d'] == 1]['y1'] - X[X['d'] == 1]['y0']).mean() &lt;= 1.1 * att_upper

ate_lower, ate_exp, ate_upper = ipw_model.estimate_ATE(X,``````

## causality

Tools for causal inference GitHub MIT Latest version published 2 years ago

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