Causal Inference Tools for a Better Evaluation of Machine Learning

• URL: http://arxiv.org/abs/2410.01392v1
• Date: Wed, 2 Oct 2024 10:03:29 GMT
• Title: Causal Inference Tools for a Better Evaluation of Machine Learning
• Authors: Michaël Soumm,
• Abstract summary: We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA) and logistic regression. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning? [39.58317527488534]
  This study compares the effectiveness of classical statistical techniques and machine learning methods for improving reliability assessments. We aim to demonstrate that classical statistical algorithms often yield more precise and interpretable results than black-box machine learning approaches.
  arXiv  Detail & Related papers  (2024-10-05T17:31:18Z)
• A Benchmark for Fairness-Aware Graph Learning [58.515305543487386]
  We present an extensive benchmark on ten representative fairness-aware graph learning methods. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods.
  arXiv  Detail & Related papers  (2024-07-16T18:43:43Z)
• Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory [0.0]
  We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness.
  arXiv  Detail & Related papers  (2024-04-18T21:22:42Z)
• Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review [1.3812010983144802]
  This paper presents a systematic review on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining. We provide a comprehensive overview of the current methodologies and their applications across various application domains. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for process analytics.
  arXiv  Detail & Related papers  (2023-12-29T12:43:43Z)
• Metric Tools for Sensitivity Analysis with Applications to Neural Networks [0.0]
  Explainable Artificial Intelligence (XAI) aims to provide interpretations for predictions made by Machine Learning models. In this paper, a theoretical framework is proposed to study sensitivities of ML models using metric techniques. A complete family of new quantitative metrics called $alpha$-curves is extracted.
  arXiv  Detail & Related papers  (2023-05-03T18:10:21Z)
• Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics [0.0]
  We develop six distinct model-agnostic metrics designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects of model explainability, ranging from local importance, global importance, and surrogate predictions. We demonstrate the practical utility of these metrics on classification and regression tasks, and integrate these metrics into an existing Python package for public use.
  arXiv  Detail & Related papers  (2023-02-23T15:28:36Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.