targeted: Targeted Inference

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) doi:10.1111/j.1541-0420.2005.00377.x ), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) doi:10.2202/1557-4679.1008 ), estimators for risk differences and relative risks (Richardson et al. (2017) doi:10.1080/01621459.2016.1192546 ), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) doi:10.1111/rssb.12504 ).

References

Bang & Robins (2005) Doubly Robust Estimation in Missing Data and Causal Inference Models, Biometrics.

Vansteelandt & Dukes (2022) Assumption-lean inference for generalised linear model parameters, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Thomas S. Richardson, James M. Robins & Linbo Wang (2017) On Modeling and Estimation for the Relative Risk and Risk Difference, Journal of the American Statistical Association.

Mark J. van der Laan (2006) Statistical Inference for Variable Importance, The International Journal of Biostatistics.

See also

Useful links:

• https://kkholst.github.io/targeted/

• Report bugs at https://github.com/kkholst/targeted/issues

Author

Maintainer: Klaus K. Holst klaus@holst.it

Authors:

• Benedikt Sommer benediktsommer92@gmail.com

• Andreas Nordland andreasnordland@gmail.com

Klaus K. Holst (Maintainer) klaus@holst.it

Examples

if (FALSE) { # \dontrun{
example(riskreg)
example(cate)
example(ate)
example(calibration)
} # }