Summary of Confounded Budgeted Causal Bandits, by Fateme Jamshidi et al.
Confounded Budgeted Causal Bandits
by Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of identifying optimal interventions in complex systems modeled by their underlying causal relationships. The goal is to find interventions that maximize rewards while operating within a budget constraint. By casting the problem as a stochastic multi-armed bandit with side information, the authors develop an algorithm that balances observations and interventions based on their costs. This approach generalizes existing methods by allowing for non-uniform costs and hidden confounders in the causal graph. The paper also presents algorithms for minimizing cumulative regret and simple regret in budgeted settings with non-uniform costs. Empirical evaluations demonstrate the superiority of these algorithms over state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists try to find the best ways to make positive changes in complex systems. They want to figure out which actions will bring the most rewards while staying within a certain budget. To do this, they use a special kind of math problem called a stochastic multi-armed bandit with extra information. This helps them decide when to take observations and when to intervene based on how much those things cost. The new approach is better than what’s been done before because it can handle different costs for different actions and hidden patterns in the system. |
