Abstract

Artificial neural networks (NN) can help researchers estimate propensity scores for quasi-experimental estimation of treatment effects because they can automatically detect complex interactions involving many covariates. However, NN is difficult to implement due to the complexity of choosing an algorithm for various treatment levels and monitoring model performance. This research aims to develop a tutorial to facilitate the use of NN to derive causal inferences. The tutorial provides social scientists with a gentle overview of machine learning terminology and best practices for training, validating, and testing NN to estimate propensity scores. The veracity of NN is demonstrated in this study using data on 5,770 teachers from the Beginner Teacher Longitudinal Study. Propensity score analysis was used to estimate the effects of assigning mentors to new teachers on the probability of continuing in the teaching profession. The results show that NN provided a better covariate balance between treatment versions than multinomial logistic regression and generalized boosted modeling. The study's findings align with previous research showing NN's advantages over conventional propensity score estimation methods.

摘要

人工神经网络（NN）可以帮助研究人员估算治疗效果的准得分，因为它们可以自动检测涉及许多协变量的复杂相互作用。然而，由于为各种治疗水平选择算法和监测模型性能的复杂性，NN难以实施。这项研究旨在开发一个教程，以促进使用NN来导出因果推论。该教程为社会科学家提供了有关机器学习术语和最佳实践的简要概述，以训练，验证和测试NN以估计倾向得分。本研究使用来自初学者纵向研究的5770名教师的数据证明了NN的准确性。倾向得分分析用于估计向新教师分配导师对继续从事教学职业的可能性的影响。结果表明，与多项式逻辑回归和广义提升模型相比，神经网络在治疗版本之间提供了更好的协变量平衡。该研究的发现与先前的研究相吻合，后者表明了NN优于传统倾向得分估计方法。