Summary of Embracing the Black Box: Heading Towards Foundation Models For Causal Discovery From Time Series Data, by Gideon Stein et al.
Embracing the black box: Heading towards foundation models for causal discovery from time series data
by Gideon Stein, Maha Shadaydeh, Joachim Denzler
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to causal discovery from time series data is proposed in this paper, which leverages deep learning techniques and end-to-end learning paradigm. The authors introduce Causal Pretraining, a methodology that learns a direct mapping from multivariate time series to underlying causal graphs in a supervised manner. Experimental results demonstrate the feasibility of causal discovery using this approach, with performance increasing with data and model size. The paper also provides examples of successful causal discovery for real-world data using causally pretrained neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper discovers hidden causes behind complex systems by analyzing time series data. Researchers used special computer models to find patterns in the data that can reveal how things are connected. They tested this method and found it works well when they had a lot of data and big computer models. This could be important for many fields, like predicting weather or understanding how diseases spread. |
Keywords
* Artificial intelligence * Deep learning * Pretraining * Supervised * Time series
