6 papers · 1 filter
Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
Wenxin Chen, Weishen Pan, Kyra Gan +1
Comparative evaluation of multiple dynamic treatment policies is essential for healthcare and policy decisions, yet conventional longitudinal causal inference methods estimate each…
Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
Shixing Yu, Promit Ghosal, Kyra Gan
A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case study. This requires token-le…
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
Sujai Hiremath, Dominik Janzing, Philipp Faller +4
Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guar…
Clustering by Denoising: Latent plug-and-play diffusion for single-cell data
Dominik Meier, Shixing Yu, Sagnik Nandy +2
Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity. Yet, clustering accuracy, and with it downstream analyses based on cell labels, remain challengi…
When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
Dominik Meier, Sujai Hiremath, Promit Ghosal +1
Distinguishing cause and effect from bivariate observational data is a foundational problem in many disciplines, but challenging without additional assumptions. Additive noise mode…
LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery
Sujai Hiremath, Promit Ghosal, Kyra Gan
Inferring causal relationships from observational data is crucial when experiments are costly or infeasible. Additive noise models (ANMs) enable unique directed acyclic graph (DAG)…