<p>Inverse probability weighting offers a valuable tool to eliminate the impact of endogenous treatment selection and attain unbiased causal treatment effect estimation in observational studies. In practice, to improve the estimation efficiency, researchers are often advocated to conduct sensible integrative analysis with multi-site studies. In such setting, how to avoid utilizing individual-level data directly is a key concern due to privacy concerns, regulatory constraints or other reasons. This work concerns multi-site current status data and provides an inverse probability weighted estimator of causal treatment effect with the additive hazards model. In particular, we develop a two-stage distributed estimation approach involving artificial neural networks and a combined estimating equation that mainly leverages summary statistics provided by each site. Asymptotic properties of the proposed estimator, including root-<i>n</i> consistency and asymptotic normality, are established. Extensive simulation studies demonstrate that the proposed method can reasonably adjust the endogenous treatment selection and is comparable to the causal method based on pooled individual-level data regarding estimation accuracy and efficiency. Moreover, the proposed estimator is more efficient than that of a single data-contributing site, manifesting the practical utility of using integrative analysis. An application to a real world data set is also provided.</p>