PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines
Summary: PipeLens diagnoses malfunctioning data-science pipelines via causal interventions on DAG-structured modules and parameters, learning a utility proxy from successful/failed runs. It identifies causally verified root causes and efficient repairs, outperforming baselines across real datasets. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Jahid Hasan (Purdue University)
- 2. Stanley Jiang (Cornell University)
- 3. Tejendra Singh (Purdue University)
- 4. Sainyam Galhotra (Cornell University)
- 5. Romila Pradhan (Purdue University)
- 6. Divesh Srivastava (AT&T)
BibTeX Citation
@article{hasan_vldb26,
title = {{PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines}},
author = {Hasan, Jahid and Jiang, Stanley and Singh, Tejendra and Galhotra, Sainyam and Pradhan, Romila and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3188--3201},
doi = {10.14778/3836663.3836682},
url = {https://doi.org/10.14778/3836663.3836682},
year = {2026}
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