Summary of Causal Inference with Latent Variables: Recent Advances and Future Prospectives, by Yaochen Zhu et al.
Causal Inference with Latent Variables: Recent Advances and Future Prospectives
by Yaochen Zhu, Yinhan He, Jing Ma, Mengxuan Hu, Sheng Li, Jundong Li
First submitted to arxiv on: 20 Jun 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 surveys recent developments in causality inference (CI) when important variables are unobserved or missing. Traditional CI methods assume all relevant variables are measured, but this is often not the case. The lack of observed confounders, mediators, and exogenous variables can lead to biased estimates, incomplete understanding of causal mechanisms, and a lack of individual-level consideration. To address these challenges, the paper discusses various CI strategies for handling latent variables, including circumvention and inference-based methods. These approaches cover tasks such as causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery, even in graph data with interference among units. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a big problem in understanding cause-and-effect relationships. When we try to figure out why something happens, we often don’t have all the information. This makes it hard to get accurate answers. The paper looks at ways to solve this problem by developing new methods for studying cause-and-effect relationships even when some important details are missing. It’s like trying to solve a puzzle with some pieces missing – you need clever strategies to fill in the gaps. |
Keywords
» Artificial intelligence » Inference
