ebal - Entropy Reweighting to Create Balanced Samples
Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
Last updated 3 years ago
2.95 score 2 stars 1 dependents 149 scripts 893 downloadsKRLS - Kernel-Based Regularized Least Squares
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Last updated 8 years ago
2.83 score 3 stars 45 scripts 280 downloads