Summary of Targeted Cause Discovery with Data-driven Learning, by Jang-hyun Kim et al.
Targeted Cause Discovery with Data-Driven Learning
by Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 The proposed machine learning approach aims to infer causal variables of a target variable from observations, identifying both direct and indirect causes within a system. The method employs a neural network trained on simulated data using supervised learning. This allows for efficient regulation of the target variable when intervening on each causal variable is difficult or costly. The local-inference strategy achieves linear complexity with respect to the number of variables, making it scalable up to thousands of variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to efficiently regulate a target variable by identifying both direct and indirect causes within a system. A novel machine learning approach uses a neural network trained on simulated data through supervised learning. This allows for efficient regulation when intervening on each causal variable is difficult or costly. |
Keywords
» Artificial intelligence » Inference » Machine learning » Neural network » Supervised
