Summary of On the Parameter Identifiability Of Partially Observed Linear Causal Models, by Xinshuai Dong and Ignavier Ng and Biwei Huang and Yuewen Sun and Songyao Jin and Roberto Legaspi and Peter Spirtes and Kun Zhang
On the Parameter Identifiability of Partially Observed Linear Causal Models
by Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 examines the parameter identifiability of linear causal models when only a subset of variables are observed. It investigates whether edge coefficients can be recovered given the causal structure and partially observed data. The model allows for flexibly related variables, both observed and latent, and considers all edge coefficients, unlike previous works that focused on edges between observed variables. Theoretical results identify three types of indeterminacy for parameters in partially observed linear causal models, and graphical conditions are proposed to ensure identifiability. A novel likelihood-based parameter estimation method is also introduced to address variance indeterminacy of latent variables. Empirical studies validate the identifiability theory and the effectiveness of the proposed method on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well we can understand cause-and-effect relationships when we only have some of the information. They use a special kind of model to help us figure out what’s going on. The model lets variables be connected in different ways, both for things we can measure and things we can’t. The researchers want to know if we can still learn about the connections between these variables even when we’re missing some of the information. They found that sometimes it’s hard to understand the relationships because of the way the data is structured. To help with this problem, they came up with a new way to analyze the data and test their ideas on fake and real datasets. |
Keywords
» Artificial intelligence » Likelihood
