
Predicting sepsis in multisite, multinational intensive care cohorts using deep learning
Despite decades of clinical research, sepsis remains a global public hea...
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Efficient Multiple Testing Adjustment for Hierarchical Inference
Hierarchical inference in (generalized) regression problems is powerful ...
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Regularizing Double Machine Learning in Partially Linear Endogenous Models
We estimate the linear coefficient in a partially linear model with conf...
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Distributional Anchor Regression
Prediction models often fail if train and test data do not stem from the...
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Perturbations and Causality in Gaussian Models
Causal inference is understood to be a very challenging problem with obs...
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Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation
We consider estimation of undirected Gaussian graphical models and inver...
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Domain adaptation under structural causal models
Domain adaptation (DA) arises as an important problem in statistical mac...
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Optimistic search strategy: Change point detection for largescale data via adaptive logarithmic queries
As a classical and ever reviving topic, change point detection is often ...
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Deconfounding and Causal Regularization for Stability and External Validity
We review some recent work on removing hidden confounding and causal reg...
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Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
In this discussion, we compare the choice of seeded intervals and that o...
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Multicarving for highdimensional postselection inference
We consider postselection inference for highdimensional (generalized) ...
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Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
We propose an adaptation of the Random Forest algorithm to estimate the ...
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Doubly Debiased Lasso: HighDimensional Inference under Hidden Confounding and Measurement Errors
Inferring causal relationships or related associations from observationa...
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Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
In recent years, there has been an increasing demand on efficient algori...
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Stabilizing Variable Selection and Regression
We consider regression in which one predicts a response Y with a set of ...
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Doubleestimationfriendly inference for highdimensional misspecified models
All models may be wrong—but that is not necessarily a problem for infere...
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Group Inference in High Dimensions with Applications to Hierarchical Testing
Group inference has been a longstanding question in statistics and the ...
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Goodnessoffit testing in highdimensional generalized linear models
We propose a family of tests to assess the goodnessoffit of a highdim...
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Change point detection for graphical models in presence of missing values
We propose estimation methods for change points in highdimensional cova...
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Invariance, Causality and Robustness
We discuss recent work for causal inference and predictive robustness in...
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Estimating heterogeneous treatment effects in nonstationary time series with statespace models
Randomized trials and observational studies, more often than not, run ov...
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Spectral Deconfounding and Perturbed Sparse Linear Models
Standard highdimensional regression methods assume that the underlying ...
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groupICA: Independent component analysis for grouped data
We introduce groupICA, a novel independent component analysis (ICA) algo...
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Hierarchical inference for genomewide association studies: a view on methodology with software
We provide a view on highdimensional statistical inference for genomew...
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Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
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Kernelbased Tests for Joint Independence
We investigate the problem of testing whether d random variables, which ...
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Distributional Equivalence and Structure Learning for Bowfree Acyclic Path Diagrams
We consider the problem of structure learning for bowfree acyclic path ...
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Scorebased Causal Learning in Additive Noise Models
Given data sampled from a number of variables, one is often interested i...
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Highdimensional learning of linear causal networks via inverse covariance estimation
We establish a new framework for statistical estimation of directed acyc...
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CAM: Causal additive models, highdimensional order search and penalized regression
We develop estimation for potentially highdimensional additive structur...
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Structural Intervention Distance (SID) for Evaluating Causal Graphs
Causal inference relies on the structure of a graph, often a directed ac...
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Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written...
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MissForest  nonparametric missing value imputation for mixedtype data
Modern data acquisition based on highthroughput technology is often fac...
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Highdimensional covariance estimation based on Gaussian graphical models
Undirected graphs are often used to describe high dimensional distributi...
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On the conditions used to prove oracle results for the Lasso
Oracle inequalities and variable selection properties for the Lasso in l...
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Pvalues for highdimensional regression
Assigning significance in highdimensional regression is challenging. Mo...
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Highdimensional additive modeling
We propose a new sparsitysmoothness penalty for highdimensional genera...
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