Books on Data Analytics

Causal Inference and Machine Learning: In Economics, Social, and Health Sciences - December 2025 (with Dr. Yuksel)

This book was born out of a frustration we kept hearing—especially from strong applied researchers.
“I understand causal inference.”
“I understand machine learning.”
“But no one explains clearly how to use ML for causal questions.”

Most ML books optimize prediction and stop there. Most causal inference texts stop before modern ML enters the picture. This book sits deliberately in between. What we tried to do differently:

• Treat machine learning as a tool, not a goal
• Be explicit about when prediction helps causal inference—and when it doesn’t
• Show methods under the hood with transparent R code, not black boxes
• Keep the focus on policy-relevant questions in economics, health, and social sciences

If you’re a graduate student, applied researcher, or practitioner who wants to use modern ML methods without losing the causal framework, this book was written for you.

🌐 Free HTML version: Online version 📖 Order: CRC Press

Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R -September 2023

The “causal inference” is the traditional framework for most statistics courses in social science and business fields, especially in Economics and Finance. As I tried to look at “prediction” from economists’ perspective, the book has become a “toolbox” that many social science and business students can follow and understand predictive methods beyond standard machine learning “code” applications. The book offers a new organization that helps students and faculty a smooth transition from “Inferential Statistics” to novel “prediction” methods. This transition starts with the first few sections, which offer a window where a traditional training in inferential statistics meets with data analytics that focuses on prediction.

A draft version can be accessed here: Toolbox
Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R