70 citations · 301 across the 67 of their papers we have counts for
3 papers · 1 filter
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
Muralikrishnna G. Sethuraman, Razieh Nabi, Faramarz Fekri
Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structure…
Learning Cyclic Causal Models from Incomplete Data
Muralikrishnna G. Sethuraman, Faramarz Fekri
Causal learning is a fundamental problem in statistics and science, offering insights into predicting the effects of unseen treatments on a system. Despite recent advances in this…
Structure Learning in Graphical Models from Indirect Observations
Hang Zhang, Afshin Abdi, Faramarz Fekri
This paper considers learning of the graphical structure of a -dimensional random vector using both parametric and non-parametric methods. Unlike the previous works…