3 accepted papers
We consider a non-convex optimization formulation for learning the weighted adjacency matrix $W$ of a directed acyclic graph (DAG) that uses acyclicity constraints that are functions of $|W_{ij}|^\beta$, for $\beta \in \mathbb{N}$. State-of-the-art algorithms for this problem use gradient-based Kar…
We consider the problem of computing bounds for causal queries on quasi-Markovian graphs with unobserved confounders and discrete valued observed variables, where identifiability does not hold. Existing non-parametric approaches for computing such bounds use multilinear programming (MP) formulations…
We consider the problem of computing bounds for causal inference problems with unobserved confounders, where identifiability does not hold. Existing non-parametric approaches for computing such bounds use linear programming (LP) formulations that quickly become intractable for existing solvers becau…