1 citations · 2 across the 4 of their papers we have counts for
4 papers
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl +4
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world a…
Experimental design for causal query estimation in partially observed biomolecular networks
Sara Mohammad-Taheri, Vartika Tewari, Rohan Kapre +5
Estimating a causal query from observational data is an essential task in the analysis of biomolecular networks. Estimation takes as input a network topology, a query estimation me…
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
Sara Mohammad-Taheri, Jeremy Zucker, Charles Tapley Hoyt +4
Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requir…
Graph-Sparse Logistic Regression
Alexander LeNail, Ludwig Schmidt, Johnathan Li +4
We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this al…