Machine learning and AI have long been concerned about modeling how an agent can change the world around it. However, intervening in the physical world takes effort, leading to sparsity of evidence and the corresponding gaps of credibility when an agent considers carrying out previously unseen actions. Making the most of sparse data within a combinatorial explosion of possible actions, dose levels, and waiting times requires careful thinking, akin to efforts for introducing more compositionality principles into machine learning (Andreas, 2019). The goal of this workshop is to bring together state-of-the-art ideas on how to predict effects of novel interventions and distribution shifts by exploiting original ways of composing evidence from multiple data-generation regimes.