Summary of Local Learning For Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables, by Zheng Li et al.
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
by Zheng Li, Feng Xie, Xichen Guo, Yan Zeng, Hao Zhang, Zhi Geng
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 proposes a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for latent variables. The method leverages testable independence and dependence relationships among observed variables to identify a valid adjustment set for a target causal relationship. This approach is designed to avoid bias and efficiently estimate the effect of a treatment variable on an outcome variable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on estimating causal effects from non-experimental data, which is important in many fields. To do this, they need to select the right “covariates” (things that can affect the results) to make sure their estimates aren’t biased. Most methods assume there’s no hidden information, but sometimes there is. This paper offers a new way to choose covariates that takes into account these hidden factors. |
