causalmodels

import pandas as pd import numpy as np from causalmodels.regression import Regression # Simulate some data with a known causal effect np.random.seed(42) n_samples = 1000 # Confounder Z affects both Treatment X and Outcome Y Z = np.random.normal(0, 1, n_samples) # Treatment X is affected by Z X = 0.5 * Z + np.random.normal(0, 1, n_samples) # Outcome Y is affected by X and Z Y = 2.0 * X + 1.0 * Z + np.random.normal(0, 1, n_samples) data = pd.DataFrame({'Z': Z, 'X': X, 'Y': Y}) # Initialize the Regression model # X: treatment variable, Y: outcome variable, control_variables: confounders model = Regression(data, treatment='X', outcome='Y', control_variables=['Z']) # Estimate the Average Treatment Effect (ATE) ate_estimate = model.estimate_ate() print(f"Observed data with N={n_samples} samples.") print(f"Estimated Average Treatment Effect (ATE) of X on Y, controlling for Z: {ate_estimate:.4f}")