Towards Public Administration Research Based on Interpretable Machine Learning

TL;DR

This paper explores how interpretable machine learning can improve causal inference and predictive accuracy in public administration research, emphasizing its potential to enhance theory development and knowledge translation.

Contribution

It introduces the application of interpretable machine learning in public administration, detailing its implementation and potential to complement traditional causal inference methods.

Findings

Highlights the importance of prediction in causal inference.

Demonstrates the implementation process of interpretable machine learning.

Shows potential for improved generalization and hypothesis generation.

Abstract

Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public administration and the broader social sciences. The advent of interpretable machine learning presents a significant opportunity to integrate prediction into quantitative research conducted in public administration. This article delves into the fundamental principles of interpretable machine learning while also examining its current applications in social science research. Building upon this foundation, the article further expounds upon the implementation process of interpretable machine learning, encompassing key aspects such as dataset construction, model training, model evaluation, and model interpretation. Lastly, the article explores the disciplinary value of interpretable machine learning within the field of public administration, highlighting its potential to enhance the generalization of inference, facilitate the selection of optimal explanations for phenomena, stimulate the construction of theoretical hypotheses, and provide a platform for the translation of knowledge. As a complement to traditional causal inference methods, interpretable machine learning ushers in a new era of credibility in quantitative research within the realm of public administration.