Causal
Discovery
Peer-reviewed breakthroughs defining the boundary of AI.
Causal Machine Learning
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
A comprehensive textbook on causal machine learning, covering theory, methods, and applications for data-driven decision making.
Sensitivity Analysis for Causal ML
Philipp Bach, Victor Chernozhukov, Martin Spindler, et al.
Practical sensitivity analysis methods for causal machine learning in industry applications.
Machine learning in cartel damages estimation: challenges and opportunities
Will Carpenter, Anna Lane, Joshua Hia, Steffen Reinhold, Iain Boa, Martin Spindler
Exploring machine learning applications for estimating damages in cartel cases.
Was die KI noch lernen muss (What AI Still Needs to Learn)
Martin Spindler
On the future of AI and what machine learning still needs to learn about causality.
Tutorial on DoubleML for double machine learning in Python and R
Philipp Bach, Sven Klaassen
Video tutorial demonstrating how to use the DoubleML package for causal inference in Python and R.
Datenbusiness Podcast: Mit Martin Spindler von Uni Hamburg
Martin Spindler
Podcast interview discussing Causal AI, Double Machine Learning, and the future of data-driven decision making.
Double/debiased machine learning for treatment and structural parameters
V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, J. Robins
A general framework for constructing debiased ML estimators for structural parameters.
Heterogeneity in the US gender wage gap
Philipp Bach, Victor Chernozhukov, Martin Spindler
Applying Causal ML to analyze high-dimensional patterns in labor market disparities.
Debiased machine learning of conditional average treatment effects
Vira Semenova, Victor Chernozhukov
Asymptotic theory for CATE estimation using high-performance machine learning.
Post-Selection and Post-Regularization Inference in Linear Models
Victor Chernozhukov, Christian Hansen, Martin Spindler
Valid inference after model selection in settings with many controls and instruments.
Causally Learning an Optimal Rework Policy
O. Schacht, S. Klaassen, P. Schwarz, M. Spindler, et al.
Industrial application of causal discovery for manufacturing process optimization.
Generic Machine Learning Inference on Heterogeneous Treatment Effects
V. Chernozhukov, M. Demirer, E. Duflo, I. Fernández-Val
Model-agnostic inference for key features of heterogeneous effects in experiments.
High-Dimensional Metrics
Victor Chernozhukov, Christian Hansen, Martin Spindler
A synthesis of modern econometrics meeting the Big Data era.
Double Machine Learning for Survival Analysis
Martin Spindler, et al.
Extending the DoubleML framework to complex time-to-event data structures.
“Theoretical precision is the prerequisite for practical certainty.”
Trusted by Industry Leaders
The Science of Causality & AI
Boston – Hong Kong – Hamburg – Munich
© 2026 Economic AI. All Rights Reserved.