Causal Discovery
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."