Associate Professor of Entrepreneurship and Innovation, Maastricht University

Causal inference and machine learning for the decisions entrepreneurs make.

At: School of Business and Economics and Faculty of Science and Engineering
Research: Entrepreneurship, strategy, causal machine learning
Co-founded: Causal Data Science Meeting, MovieGalaxies
Advisor: Texterous, on AI that people actually use

Jermain Kaminski in a bright university corridor at Maastricht University, in a blue blazer and pink bow tie — **School of Business and Economics**Maastricht University

Poets&Quants Best 40 Under 40 Professors — **School of Business and Economics**Maastricht University

Latest writing

A Doepfer modular synthesizer rack with yellow, teal and red patch cables plugged between the modules

Can we intervene inside a neural network?31 October 2026In machine learning, the honest answer to “why did the model do that?” remains “we can’t really tell”. That may be changing, and below is an interesting read. I think it’s intriguing for anyone who works on causality. In management…

A corridor at Tapijn with yellow, orange, red and blue study alcoves in timber frames

Causal Data Science Meeting 202616 October 2026

The Causal Data Science Meeting 2026 takes place online on 4–5 November 2026: two days of talks and discussion on causal data science, machine learning and AI, with researchers and practitioners from academia and industry. Attendance is free, and more than 1,600 participants have already registered.

Debate with Nick Huntington-Klein and Vasilis Syrgkanis on 4 November. Keynote by Teppo Felin on 5 November.

Park bench scene from the 1997 film Good Will Hunting

The Sistine Chapel Problem8 September 2025

In a time when Large Language Models (LLM) can write seemingly beautiful essays and solve hard coding problems in seconds, educators have to ask themselves a basic question: what is the most important thing about learning? The answer, which may surprise, can be found in a park bench conversation from a 1997 movie.

Three pillars of current research

1
Causality in Strategic Decision Making
Before a strategy is carried out, someone has to say why it should work. We write that reasoning down as a causal diagram, then use causal discovery and structural causal models to check it against data.
- Mapping theories of valuePreparing for submission
- Causal discovery in strategic management researchDRUID23
- Structural causal models in strategyAoM Proceedings, 2023
2
Machine Learning and Simulacra in Entrepreneurship
What can machine learning, and now language models, tell a founder? Earlier work predicted how ventures fare from their text, images and language. The newer work lets language models stand in for customers and give feedback before a product exists.
- Silicon sampling for product feedback in entrepreneurshipWorking paper, 2026
- Predicting outcomes in crowdfundingSmall Business Economics, 2020
- Learning to frameResubmit
3
Economic Complexity of Software
The software a country writes shows what it knows how to do. With Juhász, Wachs and Hidalgo, I measure how complex that code is and how it relates to a country’s development.
- The software complexity of nationsResearch Policy, 2026
- Creative complexityUnder review
- How researchers are using GitHub Innovation Graph data to reveal the “digital complexity” of nationsGitHub Blog, 2026