Relation-Stratified Sampling for Shapley Values Estimation in Relational Databases
Bibliographic record
Abstract
Shapley-like values, including the Shapley and Banzhaf values, provide a principled way to quantify how individual tuples contribute to a query result. Their exact computation, however, is intractable because it requires aggregating marginal contributions over exponentially many permutations or subsets. While sampling-based estimators have been studied in cooperative game theory, their direct use for relational query answering remains underexplored and often ignores the structure of schemas and joins. We study tuple-level attribution for relational queries through sampling and introduce Relation-Stratified Sampling (RSS). Instead of stratifying coalitions only by size, RSS partitions the sample space by a relation-wise count vector that records how many tuples are drawn from each relation. This join-aware stratification concentrates samples on structurally valid and informative coalitions and avoids strata that cannot satisfy query conditions. We further develop an adaptive variant, ARSS, that reallocates budget across strata using variance estimates obtained during sampling, improving estimator efficiency without increasing the total number of samples. We analyze these estimators, describe a practical implementation that reuses compiled views to reduce per-sample query cost, and evaluate them on TPCH workloads. Across diverse queries with multi-relation joins and aggregates, RSS and ARSS consistently outperform classical Monte Carlo (MCS) and size-based Stratified Sampling (SS), yielding lower error and variance with fewer samples. An ablation shows that relation-aware stratification and adaptive allocation contribute complementary gains, making ARSS a simple, effective, and anytime estimator for database-centric Shapley attribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".