Causal Inference Tools for a Better Evaluation of Machine Learning

TL;DR

This paper introduces a framework applying econometric statistical methods like OLS, ANOVA, and logistic regression to enhance the evaluation, interpretability, and fairness analysis of machine learning models.

Contribution

It bridges econometrics and machine learning by providing a comprehensive guide on applying statistical tools for deeper model analysis and evaluation.

Findings

Statistical methods reveal subtle model patterns.

Enhanced understanding of model behavior and fairness.

Guidelines for implementation and interpretation.

Abstract

We present a comprehensive framework for applying rigorous statistical techniques from econometrics to analyze and improve machine learning systems. We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA), and logistic regression, explaining their theoretical foundations and practical applications in machine learning evaluation. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness. We cover the mathematical principles behind each method, discuss their assumptions and limitations, and provide step-by-step instructions for their implementation. The paper also addresses how to interpret results, emphasizing the importance of statistical significance and effect size. Through illustrative examples, we demonstrate how these tools can reveal subtle patterns and interactions in machine learning models that are not apparent from traditional evaluation metrics. By connecting the fields of econometrics and machine learning, this work aims to equip readers with powerful analytical tools for more rigorous and comprehensive evaluation of AI systems. The framework presented here contributes to developing more robust, interpretable, and fair machine learning technologies.