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The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modelling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the comparative effectiveness of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that parametrize the biologically necessary bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, along with an illustration of their use in the context of anal canal cancer patients treated with radiotherapy.