A Hands-on Machine Learning Primer for Social Scientists - Math, Algorithms and Code - IZA Discussion Papers No. 17014

Aug 3, 2026

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A Hands-on Machine Learning Primer for Social Scientists addresses the steep learning curve faced by noncomputer scientists, especially social scientists, caused by the lack of a primer on core principles. The paper uses a pedagogical strategy anchored in the idea that mastering OLS enables progress to other estimators, then applies it to machine learning. Centered on a single-hidden-layer artificial neural network, it explains the Universal Approximation Theorem, explores solutions via feed-forward and back-propagation, and provides practical implementation in Python, aiming to support first-principles understanding for AI and causal ML.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages30

Reading Time1 h 16 min

AccessFree to download

Abstract

Introduction

Problem motivation for social scientists

Mathematical foundations

Universal Approximation Theorem

Algorithms

Feed-forward

Back-propagation

Implementation

Python practice

Askitas, Nikos Working Paper A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code IZA Discussion Papers, No. 17014 Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Askitas, Nikos (2024) : A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code, IZA Discussion Papers, No. 17014, Institute of Labor Economics (IZA), Bonn This Version is available at: [https://hdl.handle.net/10419/299942](https://hdl.handle.net/10419/299942) Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor maybe saved and copied foryour personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. DISCUSSION PAPER SERIES IZA DP No. 17014 A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code Nikos Askitas MAY 2024 DISCUSSION PAPER SERIES IZA DP No. 17014 A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code Nikos Askitas IZA MAY 2024 Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. ISSN: 2365-9793 IZA – Institute of Labor Economics | Schaumburg-Lippe-Straße 5–9 53113 Bonn, Germany | Phone: +49-228-3894-0
Email: [publications@iza.org](publications@iza.org) | [www.iza.org](www.iza.org) | | --- | --- | --- | IZA DP No. 17014 MAY 2024 ABSTRACT A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code* This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work your way up to any other estimator,” and apply it to Machine Learning. Focusing on a single-hidden-layer artificial neural network, the paper discusses its mathematical underpinnings, including the pivotal Universal Approximation Theorem—an essential ”existence theorem”. The exposition extends to the algorithmic exploration of solutions, specifically through “feed forward” and “back-propagation”, and

FAQs

Who is the primer designed for?

The paper targets noncomputer scientists, particularly social scientists, who face a steep learning curve in machine learning due to limited introductory material on fundamentals.

Which neural network setup does the primer focus on?

It focuses on a single-hidden-layer artificial neural network and uses it to build understanding of key mathematical and computational ideas.

What topics are covered to move from theory to practice?

The primer explains the Universal Approximation Theorem, discusses feed-forward and back-propagation algorithm exploration, and concludes with practical implementation in Python.

Aug 3, 2026

Upload by Noah