739 citations · 1.8k across the 34 of their papers we have counts for
41 papers
Machine-Learning-Powered Specification Testing in Linear Instrumental Variable Models
Cyrill Scheidegger, Malte Londschien, Peter Bühlmann
The linear instrumental variable (IV) model is widely used in observational studies, yet its validity hinges on strong assumptions. Classical specification tests such as the Sargan…
Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
Elliot H. Young, Peter Bühlmann
We develop Clustered Random Forests, a random forests algorithm for clustered data, arising from independent groups that exhibit within-cluster dependence. The leaf-wise prediction…
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learni…
Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets
Malte Londschien, Peter Bühlmann
We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condi…
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
Juan L. Gamella, Jonas Peters, Peter Bühlmann
In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers m…
Extrapolation-Aware Nonparametric Statistical Inference
Niklas Pfister, Peter Bühlmann
We define extrapolation as any type of statistical inference on a conditional function (e.g., a conditional expectation or conditional quantile) evaluated outside of the support of…