causalCmprsk - Nonparametric and Cox-based Estimation of Average Treatment Effects in Competing Risks

The causalCmprsk package is designed for estimation of average treatment effects (ATE) of point interventions/treatments on time-to-event outcomes with K competing events (K can be 1). The method assumes that there is no unmeasured confounding and uses propensity scores weighting for emulation of baseline randomization.

The causalCmprsk package provides two main functions: fit.cox which assumes the Cox proportional hazards regression for potential outcomes, and fit.nonpar that does not make any modeling assumptions for potential outcomes.


The causalCmprsk package can be installed by



The examples of how to use causalCmprsk package on real data can be found here.