Summary of Causal Bandits with General Causal Models and Interventions, by Zirui Yan et al.
Causal Bandits with General Causal Models and Interventions
by Zirui Yan, Dennis Wei, Dmitriy Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach to optimizing interventions in complex systems using causal bandits (CBs). In this framework, the goal is to minimize cumulative regret by identifying the best sequence of interventions. The authors advance existing research on CBs in three key directions: assuming unknown structural causal models, considering generalized soft interventions, and providing general upper and lower bounds on regret. These bounds are characterized by graph parameters, eluder dimension, and covering number of the function space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to optimize interventions in complex systems using a new approach called causal bandits (CBs). The goal is to find the best sequence of interventions that minimize regrets. The authors made three important contributions: they didn’t know what kind of model was used, allowed for many different kinds of interventions, and found upper and lower bounds for how well it worked. This helps us understand how good or bad our choices are. |
