In observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose-response curve can be recovered using the G-computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric assumptions, which carry significant risk of model misspecification. Nonparametric estimation in this context is challenging because in a nonparametric model these curves cannot be estimated at regular rates. We propose a nonparametric estimator of a causal dose-response curve known to be monotone that generalizes the classical least-squares isotonic regression estimator of a monotone regression function. We describe theoretical properties of our proposed estimator, including its irregular limit distribution and the potential for doubly-robust inference.