115 citations · 119 across the 11 of their papers we have counts for
7 papers · 1 filter
GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits
Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes +3
Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive…
FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents
Nitish Nagesh, Mahdi Bagheri, Amir M. Rahmani
Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-the-art tabular diffusion mo…
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8
Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information. Generating and eval…
Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
Nitish Nagesh, Elahe Khatibi, Thomas Hughes +3
Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, esta…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples
Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri +4
Synthetic healthcare data generation offers a promising solution to research limitations in clinical settings caused by privacy and regulatory constraints. However, current synthet…
FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
Nitish Nagesh, Ziyu Wang, Amir M. Rahmani
Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensiti…