Decision support in fields such as healthcare and finance requires reasoning about treatment timing. Artificial Intelligence holds great potential for supporting such decisions by estimating the causal effect of policies such as medication regimens or resource allocation schedules. However, existing methods for effect estimation are limited in their ability to handle \emph{irregular time}. While treatments and observations in data are often irregularly spaced across time, existing techniques either discretize time, which does scale gracefully, or disregard the effect of treatment time.We present a solution for effect estimation of sequential treatment times called Earliest Disagreement Q-Evaluation (EDQ). The method is based on Dynamic Programming and is compatible with flexible sequence models, such as transformers. EDQ provides accurate estimates under standard assumptions. We validate the approach through experiments on a survival time prediction task.