A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data

TL;DR

This paper introduces elsarticle.cls, a thoroughly re-written LaTeX document class designed for Elsevier journal submissions. The class is built on article.cls to minimize clashes with other packages and is configured to work smoothly with common packages such as natbib, geometry, graphicx, hyperref, and endfloat, while preserving a consistent formatting interface. It contrasts elsarticle.cls with the older elsart.cls, highlighting differences that reduce package conflicts, improve preprint and final formatting options, and support features like long title pages and structured front matter. The installation section guides users to download from Elsevier's resources or CTAN, explains where to place the class file in the TeX hierarchy, and outlines usage scenarios and options for figures, tables, mathematics, and theorems. Overall, the work provides a practical, standards-compliant pathway for preparing Elsevier-compatible LaTeX manuscripts with robust compatibility and formatting control.

Abstract

A Randomized Control Trial (RCT) is considered as the gold standard for evaluating the effect of any intervention or treatment. However, its feasibility is often hindered by ethical, economical, and legal considerations, making observational data a valuable alternative for drawing causal conclusions. Nevertheless, healthcare observational data presents a difficult challenge due to its high dimensionality, requiring careful consideration to ensure unbiased, reliable, and robust causal inferences. To overcome this challenge, in this study, we propose a novel two-stage feature selection technique called, Outcome Adaptive Elastic Net (OAENet), explicitly designed for making robust causal inference decisions using matching techniques. OAENet offers several key advantages over existing methods: superior performance on correlated and high-dimensional data compared to the existing methods and the ability to select specific sets of variables (including confounders and variables associated only with the outcome). This ensures robustness and facilitates an unbiased estimate of the causal effect. Numerical experiments on simulated data demonstrate that OAENet significantly outperforms state-of-the-art methods by either producing a higher-quality estimate or a comparable estimate in significantly less time. To illustrate the applicability of OAENet, we employ large-scale US healthcare data to estimate the effect of Opioid Use Disorder (OUD) on suicidal behavior. When compared to competing methods, OAENet closely aligns with existing literature on the relationship between OUD and suicidal behavior. Performance on both simulated and real-world data highlights that OAENet notably enhances the accuracy of estimating treatment effects or evaluating policy decision-making with causal inference.