Summary of Double Machine Learning For Adaptive Causal Representation in High-dimensional Data, by Lynda Aouar et al.
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data
by Lynda Aouar, Han Yu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO)
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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 In this paper, researchers propose a new approach to causal representation learning from observational data using an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS) method is designed to select representative points of the full raw data and provide an optimal sub-representation of the underlying data generating distribution. The authors compare three machine learning estimators, including support vector machine (SVM), deep learning (DL), and a hybrid super learner (SL) with DL, using SPSS. Simulation results show that DL with SPSS and hybrid methods outperform SVM with SPSS in terms of computational efficiency and estimation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to learn from data without needing to know the underlying cause-and-effect relationships. The researchers develop a technique called support points sample splitting (SPSS) to make learning more efficient. They test three different machine learning models, including deep learning, on real-world pension plan data and compare their performance using SPSS. The results show that some methods work better than others for certain types of problems. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Representation learning » Support vector machine
