• Raha Moraffah, Saketh Vishnubhatla, and Huan Liu. “Causal Robust Machine Learning”. Spring Briefs (2023). [PDF]
• Raha Moraffah, Shu Wan, and Huan Liu. “Machine Learning for Causal Inference”. Springer Nature (2023). [PDF]

2024

• Raha Moraffah, Shubh Khandelwal , Amrita Bhattacharjee , and Huan Liu. “Adversarial Text Purification: A Large Language Model Approach for Defense”. PAKDD (2024) [PDF]
• Raha Moraffah and Huan Liu. “A Generative Approach to Surrogate‐based Black‐box Attacks”. Under Submission [PDF]
• Raha Moraffah and Huan Liu. “Exploiting Class Probabilities for Black‐box Sentence‐level Attacks”. EACL (2024) [PDF]
• Raha Moraffah, Paras Sheth , Saketh Vishnubhatla , and Huan Liu. “Causal Feature Selection for Responsible Machine Learning”. Under submission [PDF]
• Raha Moraffah, Isabel Valera, and Huan Liu. “Adversarial Transferability Through the Lens of Causality”. Under submission [PDF]
• Raha Moraffah, Chaowei Xiao, and Huan Liu. “What Should They Look Like? Reinforcing Surrogate‐based Black‐box Attacks with Distribution Feedback”. Under submission [PDF]
• Paras Sheth, Raha Moraffah, Tharindu Kumarage, Aman Chadha, and Huan Liu. “Causality Guided Disentanglement for Cross‐Platform Hate Speech Detection”. ACM International Conference on Web Search and Data Mining (WSDM) (2024) [PDF]
• Suraj Jyothi Unni†, Raha Moraffah, and Huan Liu. “VQA‐GEN: A Visual Question Answering Benchmark for Domain Generalization”. Under submission [PDF]

2023

• Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah,and Huan Liu.“ConDA:ContrastiveDomain Adaptation for AI‐generated Text Detection”. IJCNLP‐AACL (2023), Outstanding Paper Award [PDF]
• Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu. “Towards LLM‐guided Causal Explainability for Black‐box Text Classifiers”. AAAI‐ReLM (2023) [PDF]
• Tharindu Kumarage, Paras Sheth, Raha Moraffah, Joshua Garland, and Huan Liu. “How Reliable Are AI‐Generated‐Text Detectors? An Assessment Framework Using Evasive Soft Prompts”. EMNLP2023 [PDF]
• Raha Moraffah, Amir‐Hossein Karimi, Adrienne Raglin, and Huan Liu. “Socially Responsible Machine Learning: A Causal Perspective”. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2023) [PDF]
• Paras Sheth , Tharindu Kumarage , Raha Moraffah, Aman Chadha, and Huan Liu. “Peace: Cross-platform hate speech detection‐a causality‐guided framework”. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML‐PKDD) (2023) [PDF]

2022

• Raha Moraffah and Huan Liu. “Query‐Efficient Target‐Agnostic Black‐Box Attack”. IEEE International Conference on Data Mining (ICDM) (2022) [PDF]
• Raha Moraffah, Suraj Jyothi Unni†, Adrienne Raglin, and Huan Liu. “Causal Data Fusion for Multimodal Disaster Classification in Social Media”. SBP‐BRiMS (2022) [PDF]
• Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K Selçuk Candan, and Huan Liu. “Evaluation methods and measures for causal learning algorithms”. IEEE Transactions on Artificial Intelligence (2022). [PDF]
• Paras Sheth, Raha Moraffah, KSelçuk Candan, Adrienne Raglin,and Huan Liu.“DomainGeneralization– A Causal Perspective”. arXiv preprint arXiv:2209.15177 (2022) [PDF]

2021 and Before

• Raha Moraffah, Paras Sheth†, Mansooreh Karami†, Anchit Bhattacharya, Qianru Wang†, Anique Tahir, Adrienne Raglin, and Huan Liu. “Causal inference for time series analysis: Problems, methods and evaluation”. Knowledge and Information Systems (KAIST) (2021) [PDF]
• Bahman Moraffah, Christ Richmond, Raha Moraffah, and Antonia Papandreou‐Suppappola. “Metricbayes: Measurements estimation for tracking in high clutter using bayesian nonparametrics”. Asilomar Conference on Signals, Systems, and Computers (2020) [PDF]
• Bahman Moraffah, Christ Richmond, Raha Moraffah, and Antonia Papandreou‐Suppappola. “Use of bayesian nonparametric methods for estimating the measurements in high clutter”.arXivpreprintarXiv:2012.09785 (2020) [PDF]
• Raha Moraffah, Mansooreh Karami†, Ruocheng Guo, Adrienne Raglin, and Huan Liu. “Causal interpretability for machine learning‐problems, methods and evaluation”. ACM SIGKDD Explorations (2020) [PDF]
• Raha Moraffah, Bahman Moraffah, Mansooreh Karami, Adrienne Raglin, and Huan Liu. “CAN: A Causal Adversarial Network for Learning Observational and Interventional Distributions”. Arxiv (2020) [PDF]
• Adrienne Raglin, Raha Moraffah, and Huan Liu. “Causality and Uncertainty of Information for Content Understanding”. IEEE International Conference on Cognitive Machine Intelligence (CogMI) (2020). [PDF]
• Lu Cheng, Ruocheng Guo*, Raha Moraffah*, K Selçuk Candan, Adrienne Raglin, and Huan Liu. “A practical data repository for causal learning with big data”. Benchmarking, Measuring, and Optimizing: Second BenchCouncil International Symposium, Bench (2019). [PDF]
• Raha Moraffah, Kai Shu, Adrienne Raglin, and Huan Liu. “Deep causal representation learning for unsupervised domain adaptation”. Arxiv 2019 [PDF]
• Vineeth Rakesh*, Ruocheng Guo*, Raha Moraffah, Nitin Agarwal, and Huan Liu. “Linked causal variational autoencoder for inferring paired spillover effects”. ACM International Conference on Information and Knowledge Management (CIKM) (2018). [PDF]
• Mohamed Sarwat, Raha Moraffah, Mohamed F Mokbel, and James L Avery. “Database system support for personalized recommendation applications”. International Conference on Data Engineering (ICDE) (2017). [PDF]
• Jia Yu, Raha Moraffah, and Mohamed Sarwat. “Hippo in action: Scalable indexing of a billion new york city taxi trips and beyond”. IEEE 33rd International Conference on Data Engineering (ICDE) (2017). [PDF]

• Raha Moraffah, Suraj Jyothi Unni, Adrienne Raglin, and Huan Liu. “CAUSEMMD: A multi‐modal classifi‐ cation platform for disaster relief”. SBP‐BRiMS (2022) [PDF]
• Raha Moraffah, Lu Cheng, Roucheng Guo, and Huan Liu. “CAUSE: A data repository for causal inference from observational data”. SBP‐BRiMS (2019) [PDF]