14 citations · 14 across the 3 of their papers we have counts for
4 papers
Finding Structure and Causality in Linear Programs
Matej Zečević, Florian Peter Busch, Devendra Singh Dhami +1
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure…
The Causal Loss: Driving Correlation to Imply Causation
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in ma…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Matej Zečević, Devendra Singh Dhami, Athresh Karanam +2
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consid…