When proportional hazards holds and the study design is causal, the hazard ratio can be interpreted causally. That is because the log hazard ratio is identical to the difference in cumulative incidence after proper transformation (log-log S(t)) and cumulative incidence is causal, respecting intention to treat (ITT) because it is an unconditional estimate. When there is non-proportional hazards that is modeled through time-dependent covariates, the interpretation of time-specific hazard ratios can be non-causal (and not respecting of ITT) after the first time at which the hazard ratio is allowed to change. But if one integrates the time-varying hazard function to obtain the cumulative hazard function, and uses that to for example estimate the cumulative incidence at 5 years, these unconditional estimates can be interpreted causally. So hazard ratios are still proper building blocks towards efficient and well-fitting models that can provide causal interpretations. In my view this is more interpretable than mean restricted survival time (RMST). The key is to be aware of which estimands are unconditional and which are time-dependent, the latter involving changing risk sets that lose the ITT interpretation as described so well by Hernán.