Summary of Causal Discovery Under Off-target Interventions, by Davin Choo et al.
Causal Discovery under Off-Target Interventions
by Davin Choo, Kirankumar Shiragur, Caroline Uhler
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Methodology (stat.ME); Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed stochastic intervention model addresses the issue of causal graph discovery by minimizing the number of interventions performed. The model captures scenarios such as fat-hand interventions and CRISPR gene knockouts, subsuming existing adaptive noiseless interventions in the literature. To verify the discovered causal graph, approximation algorithms with polylogarithmic competitive ratios are provided. Preliminary experimental results demonstrate the effectiveness of this approach. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been trying to figure out how things affect each other, but it’s hard when we only have observations and not direct control over what happens. This paper helps by proposing a new way to intervene in the system, which can be used to learn more about how things are connected. The goal is to use as few interventions as possible while still getting accurate results. |