Paper Info
Title
Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data.
Abstract
In biostatistics, propensity score is a common approach to analyze the imbalance of covariate and process confounding covariates to eliminate differences between groups. While there are an abundant amount of methods to compute propensity score, a common issue of them is the corrupted labels in the dataset. For example, the data collected from the patients could contain samples that are treated mistakenly, and the computing methods could incorporate them as a misleading information. In this paper, we propose a Machine Learning-based method to handle the problem. Specifically, we utilize the fact that the majority of sample should be labeled with the correct instance and design an approach to first cluster the data with spectral clustering and then sample a new dataset with a distribution processed from the clustering results. The propensity score is computed by Xgboost, and a mathematical justification of our method is provided in this paper. The experimental results illustrate that xgboost propensity scores computing with the data processed by our method could outperform the same method with original data, and the advantages of our method increases as we add some artificial corruptions to the dataset. Meanwhile, the implementation of xgboost to compute propensity score for multiple treatments is also a pioneering work in the area.
Year
Venue
Field
2018
arXiv: Methodology
Spectral clustering,Covariate,Confounding,Propensity score matching,Biostatistics,Artificial intelligence,Cluster analysis,Multiple treatments,Mathematics,Machine learning,Computation
DocType
Volume
Citations 
Journal
abs/1801.03132
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Chen Wang136193.70
suzhen wang235.12
Fuyan Shi300.34
Zaixiang Wang400.34