Linear-Time Estimators for Propensity Scores
Deepak Agarwal, Lihong Li, Alexander Smola; JMLR W&CP 15:93-100, 2011.
Abstract
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 \emph{both} 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]
