Large data sources such as electronic medical records or insurance claims databases present opportunities to study causal effects of interventions that are difficult to evaluate through experiments. One example is the management of septic patients in the ICU which involves performing several interventions in sequence, the choice of one depending on the outcome of others. Successfully evaluating the effect of these choices depends on strong assumptions such as having adjusted for all confounding variables, often referred to as having ignorability. While common wisdom says that having high-dimensional data increases the likelihood of this assumption being true, it also introduces new challenges: the more variables we use for estimating effects, the less likely that patients who received different treatments are comparable in all of them. In this talk, we discuss causal effect estimation and treatment group overlap through the lens of domain adaptation and off-policy reinforcement learning. We show that each of these disciplines share similar fundamental assumptions and challenges, and how they can benefit from cross-collaboration.