Draft:Economic AI
| Submission declined on 30 January 2026 by Lynch44 (talk). This submission's references do not show that the subject qualifies for a Wikipedia article—that is, they do not show significant coverage (not just passing mentions) about the subject in published, reliable, secondary sources that are independent of the subject (see the guidelines on the notability of people). Before any resubmission, additional references meeting these criteria should be added (see technical help and learn about mistakes to avoid when addressing this issue). If no additional references exist, the subject is not suitable for Wikipedia.
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Comment: Most of these sources are written by the company and therefore not reliable. Of the remaining 4, two were written by the company's founder (doesn't count towards notability), and the remaining two appear to make no mention of the company. Please find sources described in the note above. Lynch44 18:34, 30 January 2026 (UTC)
- Comment: In accordance with the Wikimedia Foundation's Terms of Use, I disclose that I have been paid by my employer for my contributions to this article. Lucasmg31 (talk) 17:25, 30 January 2026 (UTC)
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| Company type | GmbH (Private limited company) |
|---|---|
| Industry | Artificial intelligence, Machine learning, Data science |
| Founded | 2020 (estimated) |
| Founders | Martin Spindler |
| Headquarters | Regensburg, Germany |
Area served | Worldwide |
Key people | Prof. Dr. Martin Spindler (Director & Founder) Dr. Sven Klaassen (Head of Software Development) Dr. Philipp Bach (Head of Trainings) |
| Products | DoubleML |
| Services | Causal AI consulting, Executive training, Enterprise software |
| Website | economicai |
Economic AI GmbH (stylized as EconomicAI) is a German artificial intelligence company specializing in causal machine learning and econometrics. The company develops software and provides consulting services that apply double/debiased machine learning (DML) methods to business decision-making. Economic AI is headquartered in Regensburg, Germany, and serves clients worldwide through a network of academic and industry partners.[1]
History
[edit]Economic AI was founded by Prof. Dr. Martin Spindler, a professor of Data Science, Statistics, and Econometrics at the University of Hamburg.[2] The company emerged from academic research in causal machine learning, with the goal of making research-based causal inference methods accessible to practitioners and organizations across industries.
The company's founding team includes researchers who have contributed to foundational work in the field, including collaborations with scholars at MIT and publications in leading academic journals such as The Econometrics Journal and the Journal of the Royal Statistical Society. In 2024, Martin Spindler co-authored the comprehensive textbook Applied Causal Inference Powered by ML and AI with Victor Chernozhukov, Christian Hansen, Nathan Kallus, and Vasilis Syrgkanis, which has become a key reference in the field of causal machine learning.[3]
Products and services
[edit]DoubleML
[edit]Economic AI's primary software product is DoubleML, an open-source framework for causal inference using double/debiased machine learning methods.[4] The software is available for both Python and R programming languages.
DoubleML implements the double/debiased machine learning framework developed by Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins.[5]
The software is offered in two editions:
- Open Source Edition: Free Python and R libraries available on GitHub
- Cloud Edition: Enterprise managed platform with support for unstructured data and large-scale deployment
Consulting services
[edit]Economic AI provides consulting services across multiple industries, applying causal machine learning to business problems including:
- Dynamic pricing: Real-time price optimization using causal effect estimation
- Marketing mix modeling: Attribution analysis for marketing channels
- Clinical trials: Adaptive trial design and heterogeneous treatment effects analysis
- Financial forecasting: Causal discovery for identifying drivers of financial metrics
- Production optimization: Manufacturing process improvement through causal analysis
Training and education
[edit]The company offers executive education programs on causal machine learning, including intensive two-day training courses on the DoubleML framework.[6]
Technology
[edit]Economic AI's approach is based on Double machine learning (DML), a statistical framework that combines machine learning algorithms with econometric methods to estimate causal effects from observational data. The methodology addresses the challenge of confounding variables in high-dimensional settings by using a two-stage approach:
- Machine learning models are used to predict both the treatment variable and the outcome variable from confounders
- The residuals from these predictions are used to estimate the causal effect, reducing bias from overfitting
This approach allows for the use of flexible machine learning algorithms (such as random forests, gradient boosting, or neural networks) while maintaining valid statistical inference.[5]
Research contributions
[edit]Applied Causal Inference Powered by ML and AI
[edit]A major contribution from Economic AI's research team is the textbook Applied Causal Inference Powered by ML and AI, co-authored by Martin Spindler with Victor Chernozhukov (MIT), Christian Hansen (University of Chicago), Nathan Kallus (Cornell University), and Vasilis Syrgkanis (Microsoft Research).[3] The book provides a comprehensive treatment of modern causal inference methods powered by machine learning and artificial intelligence, covering topics from randomized experiments to observational studies, heterogeneous treatment effects, and causal discovery. It is freely available online and has become a standard reference for researchers and practitioners in the field.[7]
Academic publications
[edit]Economic AI's team has contributed to academic research in causal machine learning, with publications in peer-reviewed journals including:
- Bach, P., Chernozhukov, V., & Spindler, M. (2024). "Heterogeneity in the US gender wage gap". Journal of the Royal Statistical Society Series A
- Chernozhukov, V., Hansen, C., & Spindler, M. (2015). "Post-Selection and Post-Regularization Inference in Linear Models". American Economic Review
- Chernozhukov, V., Hansen, C., & Spindler, M. (2021). "High-Dimensional Metrics". Journal of Economic Perspectives
Martin Spindler has also contributed to public discourse on AI, including an essay titled "Was die KI noch lernen muss" (What AI Still Needs to Learn) published in the Frankfurter Allgemeine Zeitung in 2025.[8]
Leadership
[edit]| Name | Position | Background |
|---|---|---|
| Prof. Dr. Martin Spindler | Director & Founder | Professor at University of Hamburg; visiting scholar at MIT; PhD from University of Regensburg and LMU Munich |
| Dr. Sven Klaassen | Head of Software Development | PhD in Economics from University of Hamburg; MIT visiting scholar (2022); primary maintainer of DoubleML |
| Dr. Philipp Bach | Head of Trainings & Executive Education | Post-doctoral researcher at University of Hamburg; specialist in causal ML education |
Clients and partners
[edit]Economic AI has worked with enterprise clients across multiple industries, including partnerships with Novartis, Booking.com, SAP, Volkswagen, Porsche, and Škoda.[9]
See also
[edit]- Causal inference
- Double machine learning
- Econometrics
- Treatment effect
- Observational study
- Confounding
References
[edit]- ^ "Imprint". Economic AI GmbH. Retrieved 2025.
{{cite web}}: Check date values in:|access-date=(help) - ^ "Our Team". Economic AI GmbH. Retrieved 2025.
{{cite web}}: Check date values in:|access-date=(help) - ^ a b Chernozhukov, Victor; Hansen, Christian; Kallus, Nathan; Spindler, Martin; Syrgkanis, Vasilis (2024). Applied Causal Inference Powered by ML and AI. arXiv:2403.02467.
- ^ "DoubleML Documentation". Economic AI GmbH. Retrieved 2025.
{{cite web}}: Check date values in:|access-date=(help) - ^ a b Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; Duflo, Esther; Hansen, Christian; Newey, Whitney; Robins, James (2018). "Double/debiased machine learning for treatment and structural parameters". The Econometrics Journal. 21 (1): C1–C68. doi:10.1111/ectj.12097.
- ^ "DoubleML Training". Economic AI GmbH. Retrieved 2025.
{{cite web}}: Check date values in:|access-date=(help) - ^ Chernozhukov, Victor; Hansen, Christian; Kallus, Nathan; Spindler, Martin; Syrgkanis, Vasilis (2024). "Applied Causal Inference Powered by ML and AI". arXiv:2403.02467 [stat.ML].
- ^ Spindler, Martin (2025). "Was die KI noch lernen muss". Frankfurter Allgemeine Zeitung.
- ^ "Economic AI Homepage". Economic AI GmbH. Retrieved 2025.
{{cite web}}: Check date values in:|access-date=(help)
External links
[edit]- Official website
- Applied Causal Inference Powered by ML and AI – Free online textbook co-authored by Martin Spindler
- DoubleML Documentation
- DoubleML for Python on GitHub
- DoubleML for R on GitHub
| Company type | GmbH (Private limited company) |
|---|---|
| Industry | Artificial intelligence, Machine learning, Data science |
| Founded | 2020 (estimated) |
| Founders | Martin Spindler |
| Headquarters | Regensburg, Germany |
Number of locations | Boston, Hong Kong, Hamburg, Munich |
Key people | Prof. Dr. Martin Spindler (Director & Founder) Dr. Sven Klaassen (Head of Software Development) Dr. Philipp Bach (Head of Trainings) |
| Products | DoubleML |
| Services | Causal AI consulting, Executive training, Enterprise software |
| Website | economicai |
The company's founding team includes researchers who have contributed to foundational work in the field, including collaborations with scholars at MIT and publications in leading academic journals such as The Econometrics Journal and the Journal of the Royal Statistical Society.