Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data

TL;DR

This paper introduces a novel support points sample splitting method for adaptive causal representation learning in high-dimensional data, improving efficiency and accuracy in causal inference through double machine learning techniques.

Contribution

It proposes the support points sample splitting (SPSS) method for better data representation and integrates it with double machine learning for causal inference, demonstrating superior performance over traditional methods.

Findings

Deep learning with SPSS outperforms SVM in efficiency and accuracy.

Hybrid deep learning and super learner methods with SPSS outperform traditional models.

SPSS provides a more representative data split than random splitting.

Abstract

Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machine learning (DML) in causal inference. The support points are selected and split as optimal representative points of the full raw data in a random sample, in contrast to the traditional random splitting, and providing an optimal sub-representation of the underlying data generating distribution. They offer the best representation of a full big dataset, whereas the unit structural information of the underlying distribution via the traditional random data splitting is most likely not preserved. Three machine learning estimators were adopted for causal inference, support vector machine (SVM), deep learning (DL), and a hybrid super learner (SL) with deep learning (SDL), using SPSS. A comparative study is conducted between the proposed SVM, DL, and SDL representations using SPSS, and the benchmark results from Chernozhukov et al. (2018), which employed random forest, neural network, and regression trees with a random k-fold cross-fitting technique on the 401(k)-pension plan real data. The simulations show that DL with SPSS and the hybrid methods of DL and SL with SPSS outperform SVM with SPSS in terms of computational efficiency and the estimation quality, respectively.