4 citations · 5 across the 13 of their papers we have counts for
11 papers · 1 filter
Relaxing Faithfulness with Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence prope…
CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
Renzo G. Soatto, Anders Hoel, Greycen Ren +5
Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present Co…
On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
Marc Franquesa Monés, Jiaqi Zhang, Caroline Uhler
Learning causal relations from observational data is a fundamental problem with wide-ranging applications across many fields. Constraint-based methods infer the underlying causal s…
Causal Structure and Representation Learning with Biomedical Applications
Caroline Uhler, Jiaqi Zhang
Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep…
Probabilistic Factorial Experimental Design for Combinatorial Interventions
Divya Shyamal, Jiaqi Zhang, Caroline Uhler
A combinatorial intervention, consisting of multiple treatments applied to a single unit with potentially interactive effects, has substantial applications in fields such as biomed…
Meta-Dependence in Conditional Independence Testing
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
Conditional independence testing is a critical component of feature screening, invariant statistical models, and causal discovery. Many of these algorithms rely on the sequential a…
No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology
Luka Kovačević, Thomas Gaudelet, James Opzoomer +5
Despite substantial efforts, deep learning has not yet delivered a transformative impact on elucidating regulatory biology, particularly in the realm of predicting gene expression…
Causal Discovery with Fewer Conditional Independence Tests
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Many questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the wel…
Membership Testing in Markov Equivalence Classes via Independence Query Oracles
Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler
Understanding causal relationships between variables is a fundamental problem with broad impact in numerous scientific fields. While extensive research has been dedicated to learni…
Causal Discovery under Off-Target Interventions
Davin Choo, Kirankumar Shiragur, Caroline Uhler
Causal graph discovery is a significant problem with applications across various disciplines. However, with observational data alone, the underlying causal graph can only be recove…
Meek Separators and Their Applications in Targeted Causal Discovery
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Learning causal structures from interventional data is a fundamental problem with broad applications across various fields. While many previous works have focused on recovering the…