Zobrazeno 1 - 10
of 736
pro vyhledávání: '"BÜHLMANN, PETER"'
Autor:
Londschien, Malte, Bühlmann, Peter
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 condition. This is the first weak-instrument-robust subvector test for inst
Externí odkaz:
http://arxiv.org/abs/2407.15256
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regressio
Externí odkaz:
http://arxiv.org/abs/2405.04715
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 must often turn to simulated data, which yields limited information abo
Externí odkaz:
http://arxiv.org/abs/2404.11341
We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control f
Externí odkaz:
http://arxiv.org/abs/2403.03778
Autor:
Pfister, Niklas, Bühlmann, Peter
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 the conditioning variable. This type of extrapolation occurs in many
Externí odkaz:
http://arxiv.org/abs/2402.09758
We consider the problem of statistical inference on parameters of a target population when auxiliary observations are available from related populations. We propose a flexible empirical Bayes approach that can be applied on top of any asymptotically
Externí odkaz:
http://arxiv.org/abs/2312.08485
Many high-dimensional data sets suffer from hidden confounding which affects both the predictors and the response of interest. In such situations, standard regression methods or algorithms lead to biased estimates. This paper substantially extends pr
Externí odkaz:
http://arxiv.org/abs/2312.02860
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. W
Externí odkaz:
http://arxiv.org/abs/2310.16502
In recent years, there has been a growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with the squared erro
Externí odkaz:
http://arxiv.org/abs/2309.10083
Classical machine learning methods may lead to poor prediction performance when the target distribution differs from the source populations. This paper utilizes data from multiple sources and introduces a group distributionally robust prediction mode
Externí odkaz:
http://arxiv.org/abs/2309.02211