In healthcare or social sciences research, prospective observational studies are frequent, relatively easily put in place (compared to experimental randomized trial studies for instance) and can allow for different kinds of posterior analyses such as causal inferences. Average treatment effect (ATE) estimation for instance is possible through the use of propensity scores which allow to correct for treatment assignment biases in the non-randomized study design. However, a major caveat of large observational studies is their complexity and incompleteness: the covariates are often taken at different levels and stages, they can be heterogeneous – categorical, discrete, continuous – and almost inevitably contain missing values. The problem of missing values in causal inference has long been ignored and only recently gained some attention due to the non-negligible impacts in terms of power and bias induced by complete case analyses. We propose several consistent doubly robust average treatment effect estimators which directly account for missing values and compare them to complete case ATE estimators applied on imputed data, i.e. on complete data obtained by replacing every missing value by at least one plausible one, and to the recently proposed method of Kallus et al. . We assess the performance of our estimators on a large prognostic database containing detailed information about over 15,000 severely traumatized patients in France. Using the proposed ATE estimators and this database we study the effect on mortality of tranexamic acid administration to patients with traumatic brain injury in the context of critical care management.