Causal Banzhaf Value for Aggregate Query Explanations
Pouya Khani, Ira Assent · 2025
Aggregate queries are essential for summarizing data and obtaining condensed information. Explaining such queries—by identifying how specific predicates influence the result—provides deeper insights into the factors shaping query outcomes. However, existing statistical, interventional, and game theoretic explanation methods lack causal grounding, while causal methods require complete causal graphs, which are rarely available in large databases. To address this, we propose Causal Banzhaf Value (CBV): introducing causal awareness into Banzhaf values, our CBV method delivers explanations even in the absence of full causal graphs. Experiments on real world data demonstrate that CBV is computationally efficient, aligns with human intuition, and is consistent with causal explanations.