Search Results - "BUEHLMANN, Peter"

Invariance, Causality and Robustness by Bühlmann, Peter

“…We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or…”
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Statistical significance in high-dimensional linear models by BÜHLMANN, PETER

“…We propose a method for constructing p-values for general hypotheses in a high-dimensional linear model. The hypotheses can be local for testing a single…”
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Causal inference by using invariant prediction: identification and confidence intervals by Peters, Jonas, Bühlmann, Peter, Meinshausen, Nicolai

“…What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor…”
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Invariant Causal Prediction for Sequential Data by Pfister, Niklas, Bühlmann, Peter, Peters, Jonas

“…We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X 1 , ..., X d ). Classical ordinary…”
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Stability selection by Meinshausen, Nicolai, Bühlmann, Peter

“…Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional…”
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High-Dimensional Inference: Confidence Intervals, p-Values and R-Software hdi by Dezeure, Ruben, Bühlmann, Peter, Meier, Lukas, Meinshausen, Nicolai

“…We present a (selective) review of recent frequentist high-dimensional inference methods for constructing p-values and confidence intervals in linear and…”
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MissForest-non-parametric missing value imputation for mixed-type data by Stekhoven, Daniel J., Bühlmann, Peter

“…Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis…”
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Two optimal strategies for active learning of causal models from interventional data by Hauser, Alain, Bühlmann, Peter

“…From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the…”
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Discussion of "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models" by Law, Michael, Bühlmann, Peter

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CAM: CAUSAL ADDITIVE MODELS, HIGH-DIMENSIONAL ORDER SEARCH AND PENALIZED REGRESSION by Bühlmann, Peter, Peters, Jonas, Ernest, Jan

“…We develop estimation for potentially high-dimensional additive structural equation models. A key component of our approach is to decouple order search among…”
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p-Values for High-Dimensional Regression by Meinshausen, Nicolai, Meier, Lukas, Bühlmann, Peter

“…Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of…”
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GEOMETRY OF THE FAITHFULNESS ASSUMPTION IN CAUSAL INFERENCE by Uhler, Caroline, Raskutti, Garvesh, Bühlmann, Peter, Yu, Bin

“…Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main justification for imposing this assumption is that the set of…”
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High-Dimensional Graphs and Variable Selection with the Lasso by Meinshausen, Nicolai, Bühlmann, Peter

“…The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between…”
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Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements by Emmenegger, Corinne, Bühlmann, Peter

“…Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially…”
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Model selection over partially ordered sets by Taeb, Armeen, Bühlmann, Peter, Chandrasekaran, Venkat

“…In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a…”
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Boosting Algorithms: Regularization, Prediction and Model Fitting by Bühlmann, Peter, Hothorn, Torsten

“…We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including…”
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ON ASYMPTOTICALLY OPTIMAL CONFIDENCE REGIONS AND TESTS FOR HIGH-DIMENSIONAL MODELS by van de Geer, Sara, Bühlmann, Peter, Ritov, Ya'acov, Dezeure, Ruben

“…We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in…”
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MAXIMIN EFFECTS IN INHOMOGENEOUS LARGE-SCALE DATA by Meinshausen, Nicolai, Bühlmann, Peter

“…Large-scale data are often characterized by some degree of inhomogeneity as data are either recorded in different time regimes or taken from multiple sources…”
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Conditional transformation models by Hothorn, Torsten, Kneib, Thomas, Bühlmann, Peter

“…The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This…”
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CAUSAL INFERENCE IN PARTIALLY LINEAR STRUCTURAL EQUATION MODELS by Rothenhäusler, Dominik, Ernest, Jan, Bühlmann, Peter

“…We consider identifiability of partially linear additive structural equation models with Gaussian noise (PLSEMs) and estimation of distributionally equivalent…”
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