no code implementations • 17 Jun 2024 • Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause
Moreover, contrary to the post-hoc standardization of data generated by standard SCMs, we prove that linear iSCMs are less identifiable from prior knowledge on the weights and do not collapse to deterministic relationships in large systems, which may make iSCMs a useful model in causal inference beyond the benchmarking problem studied here.
no code implementations • 31 Jul 2023 • Scott Sussex, Pier Giuseppe Sessa, Anastasiia Makarova, Andreas Krause
We formalize this generalization of CBO as Adversarial Causal Bayesian Optimization (ACBO) and introduce the first algorithm for ACBO with bounded regret: Causal Bayesian Optimization with Multiplicative Weights (CBO-MW).
1 code implementation • 18 Nov 2022 • Scott Sussex, Anastasiia Makarova, Andreas Krause
How should we intervene on an unknown structural equation model to maximize a downstream variable of interest?
1 code implementation • 25 May 2022 • Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf
Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data.
1 code implementation • NeurIPS 2021 • Scott Sussex, Andreas Krause, Caroline Uhler
Causal structure learning is a key problem in many domains.
no code implementations • 14 May 2019 • Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning.