Estimating the impact of trauma treatment protocols is complicated by the high dimensional yet finite sample nature of trauma data collected from observational studies. Viscoelastic assays are highly predictive measures of hemostasis... However, the effectiveness of thromboelastography(TEG) based treatment protocols has not been statistically evaluated.To conduct robust and reliable estimation with sparse data, we built an estimation "machine" for estimating causal impacts of candidate variables using the collaborative targeted maximum loss-based estimation(CTMLE) framework.The computational efficiency is achieved by using the scalable version of CTMLE such that the covariates are pre-ordered by summary statistics of their importance before proceeding to the estimation steps.To extend the application of the estimator in practice, we used super learning in combination with CTMLE to flexibly choose the best convex combination of algorithms. By selecting the optimal covariates set in high dimension and reducing constraints in choosing pre-ordering algorithms, we are able to construct a robust and data-adaptive model to estimate the parameter of interest.Under this estimation framework, CTMLE outperformed the other doubly robust estimators(IPW,AIPW,stabilized IPW,TMLE) in the simulation study. CTMLE demonstrated very accurate estimation of the target parameter (ATE). Applying CTMLE on the real trauma data, the treatment protocol (using TEG values immediately after injury) showed significant improvement in trauma patient hemostasis status (control of bleeding), and a decrease in mortality rate at 6h compared to standard care.The estimation results did not show significant change in mortality rate at 24h after arrival. (read more)