12 citations · 30 across the 4 of their papers we have counts for
5 papers
Ancestral causal learning in high dimensions with a human genome-wide application
Umberto Noè, Bernd Taschler, Joachim Täger +2
We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationship…
Discussion of "Causal inference using invariant prediction: identification and confidence intervals" by Peters, Bühlmann and Meinshausen
Chris J. Oates, Jessica Kasza, Sach Mukherjee
Contribution to the discussion of the paper "Causal inference using invariant prediction: identification and confidence intervals" by Peters, Bühlmann and Meinshausen, to appear in…
Inferring network structure from interventional time-course experiments
Simon E. F. Spencer, Steven M. Hill, Sach Mukherjee
Graphical models are widely used to study biological networks. Interventions on network nodes are an important feature of many experimental designs for the study of biological netw…
Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation
Steven M. Hill, Sach Mukherjee
In many applications, multivariate samples may harbor previously unrecognized heterogeneity at the level of conditional independence or network structure. For example, in cancer bi…
Two-Sample Testing in High-Dimensional Models
Nicolas Städler, Sach Mukherjee
We propose novel methodology for testing equality of model parameters between two high-dimensional populations. The technique is very general and applicable to a wide range of mode…