Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting. The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <doi:10.1080/01621459.2014.956872>, treatments with three- and four- valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2015) <http://imai.princeton.edu/research/files/CBGPS.pdf>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Recently we have added the optimal CBPS which chooses the optimal balancing function and results in doubly robust and efficient estimator for the treatment effect as well as high dimensional CBPS when a large number of covariates exist.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of each treatment. The data is divided into a training and test set. The supervised Indian Buffet Process (sibp) is used to discover latent treatmentsi n the training set. The fitted model is then applied to the test set to infer the values of the latent treatments in the test set. Finally, Y is regressed on the latent treatments in the test set to estimate the causal effect of each treatment.