Summary of Modeling Latent Selection with Structural Causal Models, by Leihao Chen et al.
Modeling Latent Selection with Structural Causal Models
by Leihao Chen, Onno Zoeter, Joris M. Mooij
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME); Machine Learning (stat.ML)
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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 The paper proposes a novel approach to handling selection bias in Structural Causal Models (SCMs) from a causal perspective. The authors introduce a conditioning operation that transforms an SCM with latent selection into one without, encoding the causal semantics of the selected subpopulation. This operation preserves SCM properties like simplicity, acyclicity, and linearity, allowing for marginalization and intervention. The authors demonstrate how this tool enables generalizing classical causal inference results to include selection bias and modeling real-world problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to deal with biases in data. It introduces a new way of looking at selection bias in Structural Causal Models. This approach allows us to remove the bias and still keep important information about what’s causing things to happen. The authors show that this method works well and is useful for solving real-world problems. |
Keywords
* Artificial intelligence * Inference * Semantics
