4 papers
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…
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…
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…