3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
Alpar Turkoglu, Muralikrishnna G. Sethuraman, Faramarz Fekri
Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely o…
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
Muralikrishnna G. Sethuraman, Faramarz Fekri
Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the…
Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
Muralikrishnna G. Sethuraman, Faramarz Fekri
Understanding causal relationships between variables is fundamental across scientific disciplines. Most causal discovery algorithms rely on two key assumptions: (i) all variables a…
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan +3
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain ch…