From the 2 of 7 linked papers with an AI index.
7 papers
Relaxing Faithfulness with Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
The paper proposes using hard interventions as the primary source of information for learning causal graphs, introducing a relaxed faithfulness assumption that tolerates pathway ca…
Meta-Dependence in Conditional Independence Testing
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
The paper proposes a simple-to-compute measure of meta‑dependence among conditional independence statements using moment projections, provides a closed‑form solution for multivaria…
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…
Latent Causal Diffusions for Single-Cell Perturbation Modeling
Lars Lorch, Jiaqi Zhang, Charlotte Bunne +3
Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbati…
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…