6 citations · 12 across the 5 of their papers we have counts for
5 papers
Standardizing Structural Causal Models
Weronika Ormaniec, Scott Sussex, Lars Lorch +2
Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correla…
Adversarial Causal Bayesian Optimization
Scott Sussex, Pier Giuseppe Sessa, Anastasiia Makarova +1
In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown structural causal model to maximize a downstream reward variable. In this paper, we consider the generaliza…
Model-based Causal Bayesian Optimization
Scott Sussex, Anastasiia Makarova, Andreas Krause
How should we intervene on an unknown structural equation model to maximize a downstream variable of interest? This setting, also known as causal Bayesian optimization (CBO), has i…
Amortized Inference for Causal Structure Learning
Lars Lorch, Scott Sussex, Jonas Rothfuss +2
Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and…
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Scott Sussex, Andreas Krause, Caroline Uhler
Causal structure learning is a key problem in many domains. Causal structures can be learnt by performing experiments on the system of interest. We address the largely unexplored p…