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Counterfactual queries can be bounded using a linear programming approach that requires no complete causal graph, revealing insights even with incomplete domain knowledge.
Shifting to structural causal modeling reveals how targeted interventions can be explicitly evaluated, unlocking new insights into student learning dynamics.
Models that improve predictive accuracy in AML can simultaneously exacerbate fairness violations, revealing a critical trade-off that demands attention.
