class
causalis.scenarios.iv.model.IIVMIIVM
Bases: sklearn.base.BaseEstimator
DoubleML-style IIVM estimator for LATE with binary treatment and IV.
The model consumes :class:~causalis.data_contracts.IVCausalData, which
stores exactly one binary instrument. It cross-fits nuisance functions:
estimate(score="LATE") then solves the linear orthogonal score
returning
where the orthogonal signals are:
\phi_Y(W; \eta) &= g(1, X) - g(0, X) + \frac{Z(Y - g(1, X))}{m(X)} - \frac{(1-Z)(Y - g(0, X))}{1 - m(X)} \\
\phi_D(W; \eta) &= r(1, X) - r(0, X) + \frac{Z(D - r(1, X))}{m(X)} - \frac{(1-Z)(D - r(0, X))}{1 - m(X)}
Notes
The Local Average Treatment Effect (LATE) is the effect of the treatment among “compliers” — those whose treatment status is changed by the instrument.
Examples