# Causal Bandits for Linear Structural Equation Models

@article{Varici2022CausalBF, title={Causal Bandits for Linear Structural Equation Models}, author={Burak Varici and Karthikeyan Shanmugam and Prasanna Sattigeri and Ali Tajer}, journal={ArXiv}, year={2022}, volume={abs/2208.12764} }

This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize the cumulative regret with respect to the best intervention in hindsight. This is, naturally, posed as a causal bandit problem. The focus is on causal bandits for linear structural equation models (SEMs) and soft interventions. It is assumed that the graph’s structure is known, and it has N nodes. Two linear mechanisms, one soft intervention and one observational, are assumed…

## 2 Citations

### Combinatorial Causal Bandits without Graph Skeleton

- Computer ScienceArXiv
- 2023

An exponential lower bound of cumulative regrets for the CCB problem on general causal models is provided and a regret minimization algorithm for BGLMs even without the graph skeleton is designed and shows that it still achieves O ( √ T ln T ) expected regret.

### Model-based Causal Bayesian Optimization

- Computer ScienceArXiv
- 2022

This work proposes the model-based causal Bayesian optimization algorithm (MCBO) that learns a full system model instead of only modeling intervention-reward pairs and bound its cumulative regret, and obtains the first non-asymptotic bounds for CBO.

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