nnpiv.linear.ProxGradientVsHedge

class nnpiv.linear.ProxGradientVsHedge(lambda_theta=0.01, B=100, eta_theta='auto', eta_w='auto', n_iter=2000, tol=0.01, sparsity=None, fit_intercept=True)

Proximal-gradient learner against an \(\ell_1\) Hedge critic.

The soft-thresholded learner step is projected onto the radius-B \(\ell_1\) ball.

fit(Z, X, Y)

Fit.

Parameters

• Z (array-like) – Instrumental variables.

• X (array-like) – Feature or treatment matrix.

• Y (array-like) – Outcome values.

predict(X)

Predict.

Parameters

X (array-like) – Feature or treatment matrix.

Returns

Fitted linear function evaluated at X.

Return type

ndarray