We introduce DiFACE2 causal DAG, a framework for generating Diverse, Feasible, and Actionable Counterfactual Explanations under causal Directed Acyclic Graph (DAG) constraints. Leveraging background knowledge from an educational intervention program in post-conflict Nigeria, we construct an application-specific causal DAG to identify causal relationships and immutable features during counterfactual generation for black-box ML predictions. Using this DAG, we develop a causally constrained sparse ML model that integrates prior frameworks to generate diverse, feasible, and actionable counterfactuals for the Strengthening Education in Northeast – Early Grade Reading Assessment (SENSE-EGRA) dataset. DiFACE2 produces up to four counterfactuals per prediction, demonstrating high feasibility and actionability for categorical variables. While numeric features occasionally yield infeasible recommendations, this is mitigated by additional non-causal constraints. Although the sparse ML model yields lower accuracy and F1 scores than benchmark frameworks, this stems from the higher complexity and noisiness of the real-world dataset rather than methodological weakness. This trade-off underscores that meaningful, interpretable counterfactuals are preferable to near-perfect accuracy on simplified benchmarks. Therefore, when fairly evaluated on the same complex dataset, DiFACE2 outperforms prior methods across all counterfactual quality metrics.