6 papers
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…
An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis
Mahdi Bagheri, Clemente Velasco-Annis, Jian Wang +10
Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present…
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…