Modeling the Propensity Score with Statistical Learning
The progress of the ICT technology has produced data-sources that continuously generate datasets with different features and possibly with partial missing values. Such heterogeneity can be mended by integrating several processing blocks, but a unified method to extract conclusions from such heterogeneous datasets would bring consistent results with lower complexity. This paper proposes a flexible propensity score estimation method based on statistical learning for classification, and compared its performance against classical generalized linear methods.
KeywordsPropensity scores Missing value estimation Observational studies Statistical learning Deep learning
This study was supported in part by the New Energy and Industrial Technology Development Organization (NEDO), Japan, and by JSPS KAKENHI 26730130 and 15K12112.
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