<p>The causal effect of a treatment on a survival outcome is often of fundamental interest in scientific investigations. In the presence of unmeasured confounding, the instrumental variable (IV) is a valuable tool for estimating causal effects. In this article, we focus on modeling and inferring a family of generalized mean residual life (MRL) causal models, which are easily interpretable, for censored data. Utilizing a special characterization of the binary IV, we propose weighted estimating equations to estimate treatment effect and regression coefficients in MRL casual models. The weights are estimated using either logistic regression or kernel smoothing techniques. We establish the asymptotic properties of the proposed estimators and conduct extensive simulation studies to evaluate the finite sample performance. Finally, the proposed approach is applied to a dataset from the US Renal Data System to evaluate the causal effect of peritoneal dialysis in end-stage renal disease patients.</p>