Summary of Causal Discovery From Poisson Branching Structural Causal Model Using High-order Cumulant with Path Analysis, by Jie Qiao et al.
Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
by Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai, Zhifeng Hao
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 tackles a crucial task in various scientific and industrial scenarios: discovering the causal structure among count data. Count data arise naturally in fields like finance, neuroscience, and epidemiology. The authors focus on branching structures, which are common in these areas. They propose a Poisson Branching Structure Causal Model (PB-SCM) and use path analysis to learn the causal order. This is achieved by leveraging high-order cumulants and exploiting graphical conditions. The authors demonstrate the effectiveness of their approach through experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Count data are everywhere – finance, neuroscience, epidemiology… Counting things is important! But how do we figure out why some counts go up or down? That’s what this paper is about. They look at special kinds of counting, called branching structures. It’s like trying to understand why a population grows or shrinks. The authors create a new way to study these problems, using something called Poisson Branching Structure Causal Model (PB-SCM). They show that their method works well in experiments. |
