As causality enjoys increasing attention in various areas of machine learning, this workshop turns the spotlight on the assumptions behind the successful application of causal inference techniques. It is well known that answering causal queries from observational data requires strong and sometimes untestable assumptions. On the theoretical side, a whole host of settings as been established in which causal effects are identifiable and consistently estimable under a set of by now considered "standard" assumptions. While these can be reasonable in specific scenarios, they were often at least partially motivated by rendering estimation theoretically feasible. Such assumptions tell us what we would need to assert about the data generating process in order to be able to answer causal queries. Unfortunately, in applications we often find them taken for granted as properties that can safely be assumed to hold without further scrutiny. This starts with fundamentally untestable assumptions such as the stable unit treatment value assumption or ignorability and continues to no interference, faithfulness, positivity or overlap, no unobserved confounding and even reaches blanket one-size-fits all assumptions on the linearity of structural equations or the additivity of noise. This situation may lead practitioners to either believe that well founded causal inference is unattainable altogether, or that established off-the-shelf methods can be trusted to deliver reliable causal estimates in virtually any situation. Similarly, as ideas from causality are increasingly picked up by researchers in deep-, reinforcement-, or meta-learning, there is a risk that the role of assumptions for causal inference gets lost in translation. One of the main goals of this workshop is to help the research community and practitioners understand the concrete challenges of trustworthy assumptions for effective causal inference.