Linear-Time Estimators for Propensity Scores
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:93-100, 2011.
We present linear-time estimators for three popular covariate shift correction and propensity scoring algorithms: logistic regression(LR), kernel mean matching(KMM), and maximum entropy mean matching(MEMM). This allows applications in situations where \emphboth treatment and control groups are large. We also show that the last two algorithms differ only in their choice of regularizer (\ell_2 of the Radon Nikodym derivative vs. maximum entropy). Experiments show that all methods scale well. [pdf]