5 papers · 1 filter
Coarsening Linear Non-Gaussian Causal Models with Cycles
Francisco Madaleno, Francisco C Pereira, Alex Markham
Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal struct…
Coarsening Causal DAG Models
Francisco Madaleno, Pratik Misra, Alex Markham
Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across…
Intervening to Learn and Compose Causally Disentangled Representations
Alex Markham, Isaac Hirsch, Jeri A. Chang +2
In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this pa…
Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling
Xudong Sun, Alex Markham, Pratik Misra +1
Unbiased data synthesis is crucial for evaluating causal discovery algorithms in the presence of unobserved confounding, given the scarcity of real-world datasets. A common approac…
Scalable Structure Learning for Sparse Context-Specific Systems
Felix Leopoldo Rios, Alex Markham, Liam Solus
Several approaches to graphically representing context-specific relations among jointly distributed categorical variables have been proposed, along with structure learning algorith…