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Trends in Explanations: Understanding and Debugging Data-driven Systems., , and . Found. Trends Databases, 11 (3): 226-318 (2021)Learning to Sample: Counting with Complex Queries., , , and . Proc. VLDB Endow., 13 (3): 390-402 (2019)CaJaDE: Explaining Query Results by Augmenting Provenance with Context., , , , and . Proc. VLDB Endow., 15 (12): 3594-3597 (2022)DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms., , , , and . Proc. VLDB Endow., 17 (1): 65-78 (2023)Circuits for Datalog Provenance., , , and . ICDT, page 201-212. OpenProceedings.org, (2014)Exact Model Counting of Query Expressions: Limitations of Propositional Methods., , , and . ACM Trans. Database Syst., 42 (1): 1:1-1:46 (2017)Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference., , , , , , , , and . AAAI, page 22331-22340. AAAI Press, (2024)2022 ACM PODS Alberto O. Mendelzon Test-of-Time Award., , and . PODS, page 103-104. ACM, (2022)Computing Local Sensitivities of Counting Queries with Joins., , , and . SIGMOD Conference, page 479-494. ACM, (2020)Causal Relational Learning., , , , , and . SIGMOD Conference, page 241-256. ACM, (2020)