Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Rothenhäusler, Dominik"'
Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invariant across po
Externí odkaz:
http://arxiv.org/abs/2412.08869
Autor:
Jeong, Yujin, Rothenhäusler, Dominik
Many existing approaches for estimating parameters in settings with distributional shifts operate under an invariance assumption. For example, under covariate shift, it is assumed that p(y|x) remains invariant. We refer to such distribution shifts as
Externí odkaz:
http://arxiv.org/abs/2404.18370
Many researchers have identified distribution shift as a likely contributor to the reproducibility crisis in behavioral and biomedical sciences. The idea is that if treatment effects vary across individual characteristics and experimental contexts, t
Externí odkaz:
http://arxiv.org/abs/2309.01056
Many existing approaches for generating predictions in settings with distribution shift model distribution shifts as adversarial or low-rank in suitable representations. In various real-world settings, however, we might expect shifts to arise through
Externí odkaz:
http://arxiv.org/abs/2306.02948
Autor:
Jin, Ying, Rothenhäusler, Dominik
This paper develops a new framework, called modular regression, to utilize auxiliary information -- such as variables other than the original features or additional data sets -- in the training process of linear models. At a high level, our method fo
Externí odkaz:
http://arxiv.org/abs/2211.10032
We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is not drawn i.i.d. from the target population. For example, unobserved
Externí odkaz:
http://arxiv.org/abs/2209.09352
Autor:
Guo, Kevin, Rothenhäusler, Dominik
In observational causal inference, exact covariate matching plays two statistical roles: (i) it effectively controls for bias due to measured confounding; (ii) it justifies assumption-free inference based on randomization tests. This paper shows that
Externí odkaz:
http://arxiv.org/abs/2204.13193
Autor:
Jeong, Yujin, Rothenhäusler, Dominik
How can we draw trustworthy scientific conclusions? One criterion is that a study can be replicated by independent teams. While replication is critically important, it is arguably insufficient. If a study is biased for some reason and other studies r
Externí odkaz:
http://arxiv.org/abs/2202.11886
In causal inference, it is common to estimate the causal effect of a single treatment variable on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates of a fixed target variable.
Externí odkaz:
http://arxiv.org/abs/2106.03024
Autor:
Gupta, Suyash, Rothenhäusler, Dominik
Common statistical measures of uncertainty such as $p$-values and confidence intervals quantify the uncertainty due to sampling, that is, the uncertainty due to not observing the full population. However, sampling is not the only source of uncertaint
Externí odkaz:
http://arxiv.org/abs/2105.03067