Covariate Balancing Propensity Score
CBMSM.fit
Covariate Balancing Propensity Score (CBPS) for Marginal Structural Mo...
CBPS.fit determines the proper routine (what kind of treatment) and ca...
Covariate Balancing Propensity Score (CBPS) Estimation
Asymptotic Variance and Confidence Interval Estimation of the ATE
Calculates the pre- and post-weighting difference in standardized mean...
Calculates the pre- and post-weighting correlations between each covar...
Calls the appropriate balance function based on the number of treatmen...
Optimal Covariate Balance
Blackwell Data for Covariate Balancing Propensity Score
Covariate Balancing Propensity Score for Instrumental Variable Estimat...
hdCBPS high dimensional CBPS method to parses the formula object and p...
LaLonde Data for Covariate Balancing Propensity Score
npCBPS.fit
Non-Parametric Covariate Balancing Propensity Score (npCBPS) Estimatio...
Plotting CBPS Estimation for Marginal Structural Models
Plotting Covariate Balancing Propensity Score Estimation
Plot the pre-and-post weighting correlations between X and T
Calls the appropriate plot function, based on the number of treatments
Print coefficients and model fit statistics
Summarizing Covariate Balancing Propensity Score Estimation
Calculate Variance-Covariance Matrix for a Fitted CBPS Object
vcov_outcome
Calculate Variance-Covariance Matrix for Outcome Model
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 optimal CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>, several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include 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 (2018) <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, 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.