One pervasive task found throughout the empirical sciences is to determine the effect of interventions from observational (non-experimental) data. It is well-understood that assumptions are necessary to perform causal inferences, which are commonly articulated through causal diagrams (Pearl, 2000). Despite the power of this approach, there are settings where the knowledge necessary to fully specify a causal diagram may not be available, particularly in complex, high-dimensional domains. In this talk, I will briefly present two recent causal effect identification results that relax the stringent requirement of fully specifying a causal diagram. The first is a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the specification of relationships among clusters of variables, while the relationships between the variables within a cluster are left unspecified [1]. The second includes a complete calculus and algorithm for effect identification from a Partial Ancestral Graph (PAG), which represents a Markov equivalence class of causal diagrams, fully learnable from observational data [2]. These approaches are expected to help researchers and data scientists to identify novel effects in real-world domains, where knowledge is largely unavailable and coarse. \[ \] References: [1] Anand, T. V., Ribeiro, A. H., Tian, J., & Bareinboim, E. (2023). Causal Effect Identification in Cluster DAGs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, No. 10, pp. 12172-12179. [2] Jaber, A., Ribeiro, A., Zhang, J., & Bareinboim, E. (2022). Causal identification under markov equivalence: Calculus, algorithm, and completeness. Advances in Neural Information Processing Systems, 35, 3679-3690.