CBPS0.23 package

Covariate Balancing Propensity Score

CBMSM.fit

CBMSM.fit

CBMSM

Covariate Balancing Propensity Score (CBPS) for Marginal Structural Mo...

CBPS.fit

CBPS.fit determines the proper routine (what kind of treatment) and ca...

CBPS

Covariate Balancing Propensity Score (CBPS) Estimation

AsyVar

Asymptotic Variance and Confidence Interval Estimation of the ATE

balance.CBPS

Calculates the pre- and post-weighting difference in standardized mean...

balance.CBPSContinuous

Calculates the pre- and post-weighting correlations between each covar...

balance.npCBPS

Calls the appropriate balance function based on the number of treatmen...

balance

Optimal Covariate Balance

Blackwell

Blackwell Data for Covariate Balancing Propensity Score

CBIV

Covariate Balancing Propensity Score for Instrumental Variable Estimat...

hdCBPS

hdCBPS high dimensional CBPS method to parses the formula object and p...

LaLonde

LaLonde Data for Covariate Balancing Propensity Score

npCBPS.fit

npCBPS.fit

npCBPS

Non-Parametric Covariate Balancing Propensity Score (npCBPS) Estimatio...

plot.CBMSM

Plotting CBPS Estimation for Marginal Structural Models

plot.CBPS

Plotting Covariate Balancing Propensity Score Estimation

plot.CBPSContinuous

Plot the pre-and-post weighting correlations between X and T

plot.npCBPS

Calls the appropriate plot function, based on the number of treatments

print.CBPS

Print coefficients and model fit statistics

summary.CBPS

Summarizing Covariate Balancing Propensity Score Estimation

vcov.CBPS

Calculate Variance-Covariance Matrix for a Fitted CBPS Object

vcov_outcome.CBPSContinuous

vcov_outcome

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.

  • Maintainer: Christian Fong
  • License: GPL (>= 2)
  • Last published: 2022-01-18