Data Science, Machine Learning, Causal Inference, AI, Linear Programming.
Nuisance parameters b = ( np.sin(np.pi * X[:, 0] * X[:, 1]) + 2 * (X[:, 2] - 0.5) ** 2 + X[:, 3] + 0.5 * X[:, 4] + X[:, 5] * X[:, 6] + X[:, 7] ** 3 + np.sin(np.pi * X[:, 8] * X[:, 9]) ) # Create binary treatment T = np.random.binomial(1, expit(b)) # treatment interactions interaction_1 = X[:, 0] *