Causal
Discovery
Avances revisados por pares que definen la frontera de la IA.
Machine learning in cartel damages estimation: challenges and opportunities.
Will Carpenter, Anna Lane, Joshua Hia, Steffen Reinhold, Iain Boa, Martin Spindler.
Recent rapid advances in artificial intelligence (AI) and machine learning have made people acutely aware of the immense potential that these technologies have to transform almost all aspects of our lives. The field of antitrust is no exception to this trend; the Stanford Computational Antitrust pro...
Künstliche Intelligenz kann Ursachen und Wirkungen nicht erkennen. Doch gibt es einen neuen Ansatz für Unternehmen, der das möglich macht.
Applied Causal Inference Powered by ML and AI (Free Book).
An introduction to the emerging fusion of machine learning and causal inference. The book introduces ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and presents Debiased Machine Learning methods to do inference in such models using modern predictive tools.
A comprehensive textbook on causal machine learning, covering theory, methods, and applications for data-driven decision making.
Sensitivity Analysis for Causal ML: A Use Case at Booking.com.
Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler.
University of Hamburg, MIT, University of Washington, Booking.com, Economic AI
Practical sensitivity analysis methods for causal machine learning in industry applications.
Tutorial on DoubleML for double machine learning in Python and R.
Michael Celentano, Guido Imbens, Ying Jin, Georgia Papadogeorgou, Ema Perkovic, Dominik Rothenhaeusler, Qingyuan Zhao.
A state-of-the-art framework for double machine learning in Python and R
The Python and R packages DoubleML implement the double/debiased machine learning framework of Chernozhukov et al. (2018) for causal machine learning. This talk serves as an introduction to the double machine learning framework and as a tutorial for the implementation in Python and R. The double mac...
Datenbusiness Podcast: Mit Martin Spindler von Uni Hamburg.
Podcast interview discussing Causal AI, Double Machine Learning, and the future of data-driven decision making.
Heterogeneity in the US gender wage gap.
Quantifying heterogeneity in the US gender wage gap using high-dimensional wage regression and double lasso on 2016 American Community Survey data.
As a measure of gender inequality, the gender wage gap has come to play an important role both in academic research and the public debate. In 2016, the majority of full-time employed women in the United States earned significantly less than comparable men. The extent to which women were affected by ...
Causally Learning an Optimal Rework Policy.
Oliver Schacht, Sven Klaassen, Philipp Schwarz, Martin Spindler, Daniel Grunbaum, Sebastian Imhof.
Applying DoubleML to estimate the causal effect of rework steps in semiconductor manufacturing and developing optimal policy strategies.
In manufacturing, rework refers to an optional step of a production process which aims to eliminate errors or remedy products that do not meet the desired quality standards. Reworking a production lot involves repeating a previous production stage with adjustments to ensure that the final product me...
Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India.
Victor Chernozhukov, Mert Demirer, Esther Duflo, Iván Fernández-Val.
Strategies to estimate and infer key features of heterogeneous effects in randomized experiments using predictive and causal machine learning methods in high-dimensional settings.
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and l...
Digital Finance—The Future of Financial Planning in Companies.
Heinrich Kögel, Martin Spindler, Helmut Wasserbacher.
Exploring how AI revolutionizes financial planning and forecasting, leading to a fundamental transformation of the finance function.
Although digital finance is high on the agenda of many CFOs, there are still only a few companies that have already successfully transformed their finance function with AI. In this article, we show how AI revolutionises the financial planning and forecasting of companies and leads to a fundamental t...
Debiased machine learning of conditional average treatment effects and other causal functions.
Vira Semenova, Victor Chernozhukov.
Estimation and inference methods for the best linear predictor of structural functions, like CATE, using modern machine learning tools and Gaussian bootstrap.
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a co...
A synthesis of modern econometrics meeting the Big Data era.
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.
Post-Selection and Post-Regularization Inference in Linear Models.
Valid inference after model selection in settings with many controls and instruments.
“Theoretical precision is the prerequisite for practical certainty.”
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