Summary of Causal Discovery with Fewer Conditional Independence Tests, by Kirankumar Shiragur et al.
Causal Discovery with Fewer Conditional Independence Tests
by Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the challenge of understanding causal relationships in science by reducing the number of conditional independence (CI) tests required to learn about the underlying causal graph. The authors show that it’s possible to learn a coarser representation of the hidden causal graph with a polynomial number of tests, which they call Causal Consistent Partition Graph (CCPG). This new approach satisfies consistency of orientations and additional constraints, and reduces to the underlying causal graph when the causal graph is identifiable. As a consequence, the authors propose an efficient algorithm for recovering the true causal graph with a polynomial number of tests in special cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how things affect each other by finding patterns in data. Currently, computers need to test many things to figure out what’s causing something else to happen. But this can be very slow and not practical for big datasets. The authors developed a new way to find these patterns using fewer tests, which is much faster and more efficient. This could help us make better predictions and decisions in areas like medicine, economics, or social sciences. |
