2 citations · 6 across the 8 of their papers we have counts for
9 papers
Pragmatic DML with AI-Learned Representations
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli +5
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is val…
A scale-free density bound for Gaussian maxima
Suhas Vijaykumar
We derive a scale-free bound on the density of the maximum of a centered Gaussian vector. The basic bound is non-uniform, depends logarithmically on the dimension, and allows any c…
Regression Adjustments for Double Randomization in Two-Sided Marketplaces
Timothy Sudijono, Lihua Lei, Lorenzo Masoero +3
Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for…
Demonstration Experiments
Guido Imbens, Lorenzo Masoero, Alexander Rakhlin +2
Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatme…
Uniform inference for kernel instrumental variable regression
Marvin Lob, Rahul Singh, Suhas Vijaykumar
Instrumental variable regression is a foundational tool for causal analysis across the social and biomedical sciences. Recent advances use kernel methods to estimate nonparametric…
Adventures in Demand Analysis Using AI
Philipp Bach, Victor Chernozhukov, Sven Klaassen +3
This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on t…
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen, Jan Teichert-Kluge, Philipp Bach +3
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose a neural network architecture…
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limit…
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs…