In the causal bandit setup, we propose a two-stage approach.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

In general, interventional data, i.e., data collected after a perturbation in

Causal discovery is a fundamental problem with applications spanning various areas in science and engineering. It is well understood that solely using observational data, one can only orient the causal graph up to its Markov equivalence class, necessitating interventional data to learn the complete causal graph. Most works in the literature design causal discovery policies with perfect interventions, i.e., they have access to infinite interventional samples. This study considers a Bayesian approach for learning causal graphs with limited interventional samples, mirroring real-world scenarios where such samples are usually costly to obtain. By leveraging the recent result of Wienöbst et al. [2023] on uniform DAG sampling in polynomial time, we can efficiently enumerate all the cut configurations and their corresponding interventional distributions of a target set, and further track their posteriors.

Neural Information Processing Systems http://nips.cc/

In general, interventional data, i.e., data collected after a perturbation in

Causal discovery from observational and interventional data is a fundamental problem and prevalent in multiple areas of science and engineering (Pearl, 2009; Spirtes et al., 2000; Peters et al., 2017). Learning the underlying causal mechanisms is essential for policy design.

Causal discovery is a fundamental problem with applications spanning various areas in science and engineering. It is well understood that solely using observational data, one can only orient the causal graph up to its Markov equivalence class, necessitating interventional data to learn the complete causal graph. Most works in the literature design causal discovery policies with perfect interventions, i.e., they have access to infinite interventional samples. This study considers a Bayesian approach for learning causal graphs with limited interventional samples, mirroring real-world scenarios where such samples are usually costly to obtain. By leveraging the recent result of Wienöbst et al. [2023] on uniform DAG sampling in polynomial time, we can efficiently enumerate all the cut configurations and their corresponding interventional distributions of a target set, and further track their posteriors.