Summary of C-xgboost: a Tree Boosting Model For Causal Effect Estimation, by Niki Kiriakidou et al.
C-XGBoost: A tree boosting model for causal effect estimation
by Niki Kiriakidou, Ioannis E. Livieris, Christos Diou
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 A novel causal inference model called C-XGBoost is introduced for estimating potential outcomes in safety-critical domains. By combining tree-based models with neural networks’ ability to learn representations useful for both treatment and non-treatment cases, C-XGBoost inherits advantages from XGBoost, such as efficient handling of missing values and regularization techniques to prevent overfitting/bias. The proposed model is evaluated using the Dolan and Moré performance profiles, post-hoc statistical tests, and non-parametric tests, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal effect estimation aims at understanding how a treatment affects an outcome. This knowledge is crucial in life-or-death situations where decisions are made based on available data. A new model called C-XGBoost helps make these predictions by combining the strengths of two powerful tools: tree-based models and neural networks. The results show that this approach works well. |
Keywords
* Artificial intelligence * Inference * Overfitting * Regularization * Xgboost
