Summary of Testing Causal Models with Hidden Variables in Polynomial Delay Via Conditional Independencies, by Hyunchai Jeong et al.
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
by Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a novel approach to testing hypothesized causal models against observational data, which is crucial for many causal inference tasks. The proposed method leverages conditional independence relations (CIs) assumed in the model and their encoding in polynomial space via causal graphs. By exploiting local Markov properties, the algorithm requires only a smaller subset of CIs to be tested, significantly reducing the computational complexity. However, existing algorithms struggle with realistic settings featuring hidden variables and non-parametric distributions, often taking exponential time to produce even a single CI constraint. To address this challenge, the authors introduce the c-component local Markov property (C-LMP) for causal graphs with hidden variables, enabling poly-time listing of relevant CIs. Experiments on real-world and synthetic data demonstrate the practicality and efficiency of the proposed algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to test ideas about how things are connected. It’s important because it helps us figure out what causes certain events or outcomes. The method uses special tools called “conditional independence relations” that help us understand how different variables are related. These relationships can be complex and hard to navigate, but the researchers have developed a new approach that makes it more efficient. They’ve tested their method on real-world and fake data and found that it works well. This could lead to important discoveries in many fields. |
Keywords
* Artificial intelligence * Inference * Synthetic data
