This paper examines the challenges of conditional ignorability, a key assumption in causal inference used to identify causal effects from observational data. It argues that assessing this assumption is more complex than often perceived, as it implicitly requires evaluating numerous structural configurations within covariate sets. To address this, the authors propose a new framework using Cluster Causal Diagrams (CG(3)), which abstracts the internal structure of covariates into three blocks: treatment (X), outcome (Y), and adjustment covariates (Z). This approach introduces structural ignorability, a concept evaluated using a modified back-door criterion on CG(3) diagrams, offering a more transparent and practical method for assessing causal assumptions. The paper highlights that while conditional ignorability cannot be reliably assessed at this level of abstraction, structural ignorability provides a principled middle ground between the traditional potential outcomes (PO) framework and comprehensive structural causal models (SCMs).