Individual effects are identifiable depending on the causal assumptions you can sustain and the type of data you can collect. In fact, we make inferences of individual causal effects all the time: if you give a glass of an unknown chemical to a perfectly healthy person, and the person suddenly dies, most people would agree that it is a reasonable inference that the chemical was the cause of death of that person (which could be later confirmed by autopsy). Can you tell what is the assumption that allows you to make this inference?
The bottom line is that identification of causal effects, be it average effects, conditional effects or individual effects depends on causal assumptions (see here and here). The difference between identifying average effects and individual effects is the strength of assumptions that you need to make. While you can make inference of average effects with just mild qualitative assumptions --- such as a DAG, with no need for parametric information about the form of the functions or distribution of error terms --- inference about individual causal effects require more details. See for instance, this answer.
Here let me give you two examples: in a linear causal model, all individual treatment effects are identified --- linearity implies constant effects, so the individual treatment effect equals the average treatment effect. Another example, related to the fine point in Hernan's book: if you treat the same individual more than once with a reversible treatment, and if you assume that the individual does not change during time periods, you can also estimate the treatment effect for that individual. So, as you can see, it all depends on the causal assumptions you can sustain and the type of data you can collect.