Abstract

The Trap of Data-Only Evidence: Understanding the Effectiveness of Your Efforts Through Cause and Effect!

- Are Ordinary Ideas Creating Value?
- Does a Higher Star Rating on a Gourmet Website Increase Reservations?
- Does the Quality of Logistics in Online Shopping Really Increase Sales?

How to Break Free from Precedent, Intuition, and Assumptions?
In our daily lives, we often hear claims that "this is effective." Recently, data utilization has become commonplace, allowing us to correctly judge explanations that claim effectiveness. However, is your initiative truly effective? Relying solely on data can lead to pitfalls. This book clearly explains the cutting edge of economics called "causal inference" and how to distinguish between "spurious effects" and "real effects." Let's decipher the relationship between cause and effect and break free from "precedent," "intuition," and "assumptions"!

Building Evidence Yourself Allows for Righteous Challenges!
In healthcare, education, and policy, evidence has been accumulated through causal inference, leading to significant progress. While it's important to base policy decisions on individual evidence, true learning lies in the process of seeking evidence. Let's leverage the changing organizational dynamics, a willingness to take on challenges, and a heightened understanding of the world—beyond simply discerning effectiveness.

This book, written by a data scientist at CyberAgent, also introduces key points for transforming "causal inference" into value in business. When is the right time to utilize causal inference? How can you gain understanding from others about effective initiatives? How can you make initiatives impactful? Your understanding of the workplace is key.

Author’s Information

- Shota Yasui
Completed his Master's degree in Economics at the Norwegian University of Economics in 2013. He joined CyberAgent in the same year. After joining the company, he worked at an advertising agency, conducting advertising effectiveness verification, and then moved to the AdTech Studio in 2015. Since then, his main work has been the application of machine learning and data analysis and effectiveness verification in situations where machine learning is used. He established the AILab Economics Group in 2016. He has also served as the Deputy Director of the Data Science Center since 2019. He has been the Chief Data Scientist since 2022. His publications include "Introduction to Effectiveness Verification" (Gijutsu Hyoronsha, 2020), "Introduction to Machine Learning for Policy Design" (co-authored, Gijutsu Hyoronsha, 2021), and "Introduction to Effectiveness Verification with Python" (supervised, Ohmsha, 2024).

- Hirotake Ito
Graduated from the Faculty of Economics at Hitotsubashi University in 2014 and completed his Master's degree at the Graduate School of Economics at the same university in 2015. Yusuke Kaneko graduated from the Faculty of Economics, University of Tokyo in 2016 and completed his master's degree in Statistics at the Graduate School of Economics, University of Tokyo in 2018 before joining CyberAgent. After working at an asset management company, a consulting firm, and as a university researcher, he is currently employed at CyberAgent. As a data scientist, he is involved in the product growth of advertising delivery systems. He is the co-author of "Introduction to Effectiveness Verification Using Python" (Ohmsha, 2024).

- Yusuke Kaneko
After graduating from the Faculty of Economics at the University of Tokyo in 2016 and completing a master’s degree in the Statistics Program at the Graduate School of Economics at the same university in 2018, he joined CyberAgent. Upon joining the company, he worked on the development of predictive models and performance validation as part of the advertising delivery system development team, and later led the launch and monetization of new advertising products. He is currently actively engaged in the societal implementation of cutting-edge technologies in collaboration with research organizations and has had papers accepted in the industrial applications track at international conferences. He has been a board member of the Data Science Center since 2021. He is a Kaggle Master. He is the co-author of "Introduction to Effectiveness Testing with Python" (Ohmsha, 2024).