On the Diagnostic Resolution of Signature Analysis. - Janusz Rajski; Jerzy Tyszer; Babak Salimi (ICCAD, 1990)

Causality in Databases, Database Repairs, and Consistency-Based Diagnosis (extended abstract). - Leopoldo E. Bertossi; Babak Salimi (AMW, 2014)

Unifying Causality, Diagnosis, Repairs and View-Updates in Databases. - Leopoldo E. Bertossi; Babak Salimi (CoRR, 2014)

Causality in Databases: The Diagnosis and Repair Connections. - Babak Salimi; Leopoldo E. Bertossi (CoRR, 2014)

Query-Answer Causality in Databases: Abductive Diagnosis and View Updates. - Babak Salimi; Leopoldo E. Bertossi (ACI@UAI, 2015)

Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints. - Babak Salimi; Leopoldo E. Bertossi (FLAIRS, 2016)

Quantifying Causal Effects on Query Answering in Databases. - Babak Salimi; Leopoldo E. Bertossi; Dan Suciu; Guy Van den Broeck (TaPP, 2016)

ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data. - Babak Salimi; Dan Suciu (CoRR, 2016)

A Demonstration of Interactive Analysis of Performance Measurements with Viska. - Helga Gudmundsdottir; Babak Salimi; Magdalena Balazinska; Dan R. K. Ports; Dan Suciu (SIGMOD Conference, 2017)

A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence. - Sudeepa Roy 001; Babak Salimi (CoRR, 2017)

Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints. - Leopoldo E. Bertossi; Babak Salimi (Int. J. Approx. Reason., 2017)

From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back. - Leopoldo E. Bertossi; Babak Salimi (Theory Comput. Syst., 2017)

ZaliQL: Causal Inference from Observational Data at Scale. - Babak Salimi; Corey Cole; Dan R. K. Ports; Dan Suciu (Proc. VLDB Endow., 2017)

Bias in OLAP Queries: Detection, Explanation, and Removal. - Babak Salimi; Johannes Gehrke; Dan Suciu (SIGMOD Conference, 2018)

MobilityMirror: Bias-Adjusted Transportation Datasets. - Luke Rodriguez; Babak Salimi; Haoyue Ping; Julia Stoyanovich; Bill Howe (BiDU@VLDB, 2018)

HypDB: Detect, Explain And Resolve Bias in OLAP. - Babak Salimi; Johannes Gehrke; Dan Suciu (CoRR, 2018)

HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries. - Babak Salimi; Corey Cole; Peter Li; Johannes Gehrke; Dan Suciu (Proc. VLDB Endow., 2018)

Interventional Fairness: Causal Database Repair for Algorithmic Fairness. - Babak Salimi; Luke Rodriguez; Bill Howe; Dan Suciu (SIGMOD Conference, 2019)

Capuchin: Causal Database Repair for Algorithmic Fairness. - Babak Salimi; Luke Rodriguez; Bill Howe; Dan Suciu (CoRR, 2019)

Data Management for Causal Algorithmic Fairness. - Babak Salimi; Bill Howe; Dan Suciu (IEEE Data Eng. Bull., 2019)

Mining Approximate Acyclic Schemes from Relations. - Batya Kenig; Pranay Mundra; Guna Prasaad; Babak Salimi; Dan Suciu (SIGMOD Conference, 2020)

Causal Relational Learning. - Babak Salimi; Harsh Parikh; Moe Kayali; Lise Getoor; Sudeepa Roy 001; Dan Suciu (SIGMOD Conference, 2020)

Demonstration of Inferring Causality from Relational Databases with CaRL. - Moe Kayali; Babak Salimi; Dan Suciu (Proc. VLDB Endow., 2020)

Database Repair Meets Algorithmic Fairness. - Babak Salimi; Bill Howe; Dan Suciu (SIGMOD Rec., 2020)

Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals. - Sainyam Galhotra; Romila Pradhan; Babak Salimi (SIGMOD Conference, 2021)

Detecting Treatment Effect Modifiers in Social Networks. - Amir Gilad; Harsh Parikh; Babak Salimi; Sudeepa Roy 001 (CoRR, 2021)

Demonstration of Generating Explanations for Black-Box Algorithms Using Lewis. - Paul Y. Wang; Sainyam Galhotra; Romila Pradhan; Babak Salimi (Proc. VLDB Endow., 2021)

Explainable AI: Foundations, Applications, Opportunities for Data Management Research. - Romila Pradhan; Aditya Lahiri; Sainyam Galhotra; Babak Salimi (ICDE, 2022)

Causal Inference in Data Analysis with Applications to Fairness and Explanations. - Sudeepa Roy 001; Babak Salimi (RW, 2022)

HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. - Sainyam Galhotra; Amir Gilad; Sudeepa Roy 001; Babak Salimi (SIGMOD Conference, 2022)

Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification. - Maliha Tashfia Islam; Anna Fariha; Alexandra Meliou; Babak Salimi (SIGMOD Conference, 2022)

Interpretable Data-Based Explanations for Fairness Debugging. - Romila Pradhan; Jiongli Zhu; Boris Glavic; Babak Salimi (SIGMOD Conference, 2022)

Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher. - Jiongli Zhu; Romila Pradhan; Boris Glavic; Babak Salimi (SIGMOD Conference, 2022)

Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces. - Kamran Alipour; Aditya Lahiri; Ehsan Adeli 001; Babak Salimi; Michael J. Pazzani (CoRR, 2022)

Combining Counterfactuals With Shapley Values To Explain Image Models. - Aditya Lahiri; Kamran Alipour; Ehsan Adeli 001; Babak Salimi (CoRR, 2022)

Crab: Learning Certifiably Fair Predictive Models in the Presence of Selection Bias. - Jiongli Zhu; Nazanin Sabri; Sainyam Galhotra; Babak Salimi (CoRR, 2022)

Database Education at UC San Diego. - Arun Kumar 001; Alin Deutsch; Amarnath Gupta; Yannis Papakonstantinou; Babak Salimi; Victor Vianu (SIGMOD Rec., 2022)

Causal What-If and How-To Analysis Using HypeR. - Fangzhu Shen; Kayvon Heravi; Oscar Gomez; Sainyam Galhotra; Amir Gilad; Sudeepa Roy 001; Babak Salimi (ICDE, 2023)

On Explaining Confounding Bias. - Brit Youngmann; Michael J. Cafarella; Yuval Moskovitch; Babak Salimi (ICDE, 2023)

SAFE-PASS: Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups. - Indrajit Ray; Bhavani Thuraisingham; Jaideep Vaidya; Sharad Mehrotra; Vijayalakshmi Atluri; Indrakshi Ray; Murat Kantarcioglu; Ramesh Raskar; Babak Salimi; Steve Simske; Nalini Venkatasubramanian; Vivek K. Singh 001 (SACMAT, 2023)

NEXUS: On Explaining Confounding Bias. - Brit Youngmann; Michael J. Cafarella; Yuval Moskovitch; Babak Salimi (SIGMOD Conference Companion, 2023)

Causal Data Integration. - Brit Youngmann; Michael J. Cafarella; Babak Salimi; Anna Zeng (Proc. VLDB Endow., 2023)

Consistent Range Approximation for Fair Predictive Modeling. - Jiongli Zhu; Sainyam Galhotra; Nazanin Sabri; Babak Salimi (Proc. VLDB Endow., 2023)

Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation. - Jensen Hwa; Qingyu Zhao; Aditya Lahiri; Adnan Masood; Babak Salimi; Ehsan Adeli 001 (CVPR Workshops, 2024)

Learning from Uncertain Data: From Possible Worlds to Possible Models. - Jiongli Zhu; Su Feng; Boris Glavic; Babak Salimi (NeurIPS, 2024)

First Workshop on Governance, Understanding and Integration of Data for Effective and Responsible AI (GUIDE-AI). - Abolfazl Asudeh; Sainyam Galhotra; Amir Gilad; Babak Salimi; Brit Youngmann (SIGMOD Conference Companion, 2024)

DEMA: Enhancing Causal Analysis through Data Enrichment and Discovery in Data Lakes. - Kayvon Heravi; Saathvik Dirisala; Babak Salimi (VLDB Workshops, 2024)

Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects. - Seyedeh Baharan Khatami; Harsh Parikh; Haowei Chen; Sudeepa Roy 001; Babak Salimi (CoRR, 2024)

Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders. - Parjanya Prashant; Seyedeh Baharan Khatami; Bruno Ribeiro 001; Babak Salimi (CoRR, 2024)

Overcoming Data Biases: Towards Enhanced Accuracy and Reliability in Machine Learning. - Jiongli Zhu; Babak Salimi (IEEE Data Eng. Bull., 2024)

OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport. - Alireza Pirhadi; Mohammad Hossein Moslemi; Alexander Cloninger; Mostafa Milani; Babak Salimi (Proc. ACM Manag. Data, 2024)

Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects. - Seyedeh Baharan Khatami; Harsh Parikh; Haowei Chen; Sudeepa Roy 001; Babak Salimi (AISTATS, 2025)

Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders. - Parjanya Prajakta Prashant; Seyedeh Baharan Khatami; Bruno Ribeiro 001; Babak Salimi (AISTATS, 2025)

Navigating Data Errors in Machine Learning Pipelines: Identify, Debug, and Learn. - Bojan Karlas; Babak Salimi; Sebastian Schelter (ICDE, 2025)

CausaLens: A System for Summarizing Causal DAGs. - Noam Chen; Anna Zeng; Michael J. Cafarella; Batya Kenig; Markos Markakis; Oren Mishali; Brit Youngmann; Babak Salimi (SIGMOD Conference Companion, 2025)

Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data. - Kaiyuan Hu; Jiongli Zhu; Boris Glavic; Babak Salimi (SIGMOD Conference Companion, 2025)

A Lightweight Method to Disrupt Memorized Sequences in LLM. - Parjanya Prashant; Kaustubh Ponkshe; Babak Salimi (CoRR, 2025)

Causal DAG Summarization. - Anna Zeng; Michael J. Cafarella; Batya Kenig; Markos Markakis; Brit Youngmann; Babak Salimi (Proc. VLDB Endow., 2025)

Using Causal Inference to Explore Government Policy Impact on Computer Usage. - Mingjia Zhu; Lechuan Wang; Julien Sebot; Bijan Arbab; Babak Salimi; Alexander Cloninger (CoRR, 2025)

Towards Robust Offline Evaluation: A Causal and Information Theoretic Framework for Debiasing Ranking Systems. - Seyedeh Baharan Khatami; Sayan Chakraborty; Ruomeng Xu; Babak Salimi (CoRR, 2025)

Causal DAG Summarization (Full Version). - Anna Zeng; Michael J. Cafarella; Batya Kenig; Markos Markakis; Brit Youngmann; Babak Salimi (CoRR, 2025)

Stress-Testing ML Pipelines with Adversarial Data Corruption. - Jiongli Zhu; Geyang Xu; Felipe Lorenzi; Boris Glavic; Babak Salimi (CoRR, 2025)

KAIROS: Scalable Model-Agnostic Data Valuation. - Jiongli Zhu; Parjanya Prajakta Prashant; Alex Cloninger; Babak Salimi (CoRR, 2025)