Data Science, Causal Inference, Machine Learning, Optimization, Notes From Industry.
# Nuisance parameters b = ( np.sin(np.pi * X[:, 0]) + 2 * (X[:, 1] - 0.5) ** 2 + X[:, 2] * X[:, 3] ) # Create treatment and treatment effect T = np.linspace(200, 10000, n) T_mm = michaelis_menten(T, alpha, lam) * tau_weight tau = T_mm / T # Calculate outcome y = b + T * tau +