CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets
Summary: Demo of CausalExplain, a model-agnostic system tracing ML predictions to training-data subsets via causal DAGs. It outputs top-k data-centric explanations from training-data predicates that causally drive the prediction, aiding debugging and insight across datasets and models. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Arman Ashkari
- 2. El Kindi Rezig
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,925 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD | 7.8877522e-05 |
| 6,948 | DataPrism: Exposing Disconnect between Data and Systems | 2022 | SIGMOD | 4.8865863e-05 |
| 6,999 | Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher | 2022 | SIGMOD | 4.8629617e-05 |
| 7,481 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 4.7135369e-05 |
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