ABSTRACT In the competing risks setting, the ‐year absolute risk for a specific time (e.g., 2 years), also called the cumulative incidence function at time , is often interesting to estimate. It is routinely estimated using the nonparametric Aalen–Johansen estimator. This estimator handles right‐censored data and has desirable large sample properties, as it is the nonparametric maximum likelihood estimator (NPMLE). Inference for comparing absolute risks, via either a risk difference or a risk ratio, can therefore be done via usual asymptotic normal approximations and the delta method. However, the small sample performances of this approach are not fully satisfactory. Especially, (i) coverage of confidence intervals may be inaccurate and (ii) comparisons made using a risk ratio and a risk difference can lead to inconsistent conclusions, in terms of statistical significance. We, therefore, introduce an alternative empirical likelihood approach. One advantage of this approach is that it always leads to consistent conclusions when comparing absolute risks via a risk ratio and a risk difference, in terms of significance. Simulation results also suggest that small sample inference using this approach can be more accurate. We present the computation of confidence intervals and p ‐values using this approach and the asymptotic properties that justify them. We provide formulas and algorithms to compute constrained NPMLE, from which empirical likelihood ratios and inference procedures are derived. The novel approach has been implemented in the timeEL package for R , and some of its advantages are demonstrated via reproducible analyses of bone marrow transplant data.