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09/18/2023
Invariant Probabilistic Prediction
In recent years, there has been a growing interest in statistical method...
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09/05/2023
Distributionally Robust Machine Learning with Multi-source Data
Classical machine learning methods may lead to poor prediction performan...
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08/20/2023
Model Selection over Partially Ordered Sets
In problems such as variable selection and graph estimation, models are ...
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07/18/2023
Causality-oriented robustness: exploiting general additive interventions
Since distribution shifts are common in real-world applications, there i...
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04/02/2023
TSCI: two stage curvature identification for causal inference with invalid instruments
TSCI implements treatment effect estimation from observational data unde...
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03/24/2023
repliclust: Synthetic Data for Cluster Analysis
We present repliclust (from repli-cate and clust-er), a Python package f...
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02/11/2023
Confidence and Uncertainty Assessment for Distributional Random Forests
The Distributional Random Forest (DRF) is a recently introduced Random F...
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11/27/2022
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
We consider the problem of recovering the causal structure underlying ob...
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10/20/2022
On the pitfalls of Gaussian likelihood scoring for causal discovery
We consider likelihood score based methods for causal discovery in struc...
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10/13/2022
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LS...
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09/19/2022
Distributionally robust and generalizable inference
We discuss recently developed methods that quantify the stability and ge...
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06/29/2022
Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning
Causal inference methods for treatment effect estimation usually assume ...
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05/18/2022
Ancestor regression in linear structural equation models
We present a new method for causal discovery in linear structural equati...
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05/10/2022
Random Forests for Change Point Detection
We propose a novel multivariate nonparametric multiple change point dete...
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03/24/2022
Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables
Instrumental variables regression is a popular causal inference method f...
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11/29/2021
A Fast Non-parametric Approach for Causal Structure Learning in Polytrees
We study the problem of causal structure learning with no assumptions on...
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11/08/2021
The Weighted Generalised Covariance Measure
We introduce a new test for conditional independence which is based on w...
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09/29/2021
Assessing the goodness of fit of linear regression via higher-order least squares
We introduce a simple diagnostic test for assessing the goodness of fit ...
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08/31/2021
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
Traditionally, spline or kernel approaches in combination with parametri...
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08/19/2021
Structure Learning for Directed Trees
Knowing the causal structure of a system is of fundamental interest in m...
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08/02/2021
ricu: R's Interface to Intensive Care Data
Providing computational infrastructure for handling diverse intensive ca...
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07/12/2021
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Despite decades of clinical research, sepsis remains a global public hea...
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04/30/2021
Efficient Multiple Testing Adjustment for Hierarchical Inference
Hierarchical inference in (generalized) regression problems is powerful ...
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01/29/2021
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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01/20/2021
Distributional Anchor Regression
Prediction models often fail if train and test data do not stem from the...
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01/18/2021
Perturbations and Causality in Gaussian Models
Causal inference is understood to be a very challenging problem with obs...
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01/06/2021
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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10/29/2020
Domain adaptation under structural causal models
Domain adaptation (DA) arises as an important problem in statistical mac...
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10/20/2020
Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries
As a classical and ever reviving topic, change point detection is often ...
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08/14/2020
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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06/23/2020
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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06/08/2020
Multicarving for high-dimensional post-selection inference
We consider post-selection inference for high-dimensional (generalized) ...
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05/29/2020
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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04/08/2020
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding and Measurement Errors
Inferring causal relationships or related associations from observationa...
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02/16/2020
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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11/05/2019
Stabilizing Variable Selection and Regression
We consider regression in which one predicts a response Y with a set of ...
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09/24/2019
Double-estimation-friendly inference for high-dimensional misspecified models
All models may be wrong—but that is not necessarily a problem for infere...
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09/04/2019
Group Inference in High Dimensions with Applications to Hierarchical Testing
Group inference has been a long-standing question in statistics and the ...
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08/09/2019
Goodness-of-fit testing in high-dimensional generalized linear models
We propose a family of tests to assess the goodness-of-fit of a high-dim...
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07/11/2019
Change point detection for graphical models in presence of missing values
We propose estimation methods for change points in high-dimensional cova...
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12/19/2018
Invariance, Causality and Robustness
We discuss recent work for causal inference and predictive robustness in...
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12/10/2018
Estimating heterogeneous treatment effects in nonstationary time series with state-space models
Randomized trials and observational studies, more often than not, run ov...
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11/13/2018
Spectral Deconfounding and Perturbed Sparse Linear Models
Standard high-dimensional regression methods assume that the underlying ...
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06/04/2018
groupICA: Independent component analysis for grouped data
We introduce groupICA, a novel independent component analysis (ICA) algo...
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05/08/2018
Hierarchical inference for genome-wide association studies: a view on methodology with software
We provide a view on high-dimensional statistical inference for genome-w...
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01/18/2018
Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
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03/01/2016
Kernel-based Tests for Joint Independence
We investigate the problem of testing whether d random variables, which ...
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08/07/2015
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
We consider the problem of structure learning for bow-free acyclic path ...
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11/25/2013
Score-based Causal Learning in Additive Noise Models
Given data sampled from a number of variables, one is often interested i...
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11/14/2013