Christina Heinze-Deml
Christina Heinze-Deml
Verified email at stat.math.ethz.ch - Homepage
Title
Cited by
Cited by
Year
Invariant causal prediction for nonlinear models
C Heinze-Deml, J Peters, N Meinshausen
Journal of Causal Inference 6 (2), 2018
1032018
Causal structure learning
C Heinze-Deml, MH Maathuis, N Meinshausen
Annual Review of Statistics and Its Application 5, 371-391, 2018
742018
Conditional variance penalties and domain shift robustness
C Heinze-Deml, N Meinshausen
Machine Learning 110 (2), 303-348, 2021
72*2021
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
C Heinze, B McWilliams, N Meinshausen
Proceedings of the 19th International Conference on Artificial Intelligence …, 2016
392016
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
D Rothenhäusler, C Heinze, J Peters, N Meinshausen
arXiv preprint arXiv:1506.02494, 2015
382015
Random projections for large-scale regression
GA Thanei, C Heinze, N Meinshausen
Big and complex data analysis, 51-68, 2017
312017
Loco: Distributing ridge regression with random projections
C Heinze, B McWilliams, N Meinshausen, G Krummenacher
arXiv preprint arXiv:1406.3469, 2014
30*2014
Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells
S Triantafillou, V Lagani, C Heinze-Deml, A Schmidt, J Tegner, ...
Scientific reports 7 (1), 1-11, 2017
232017
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
F Yang, Z Wang, C Heinze-Deml
arXiv preprint arXiv:1906.11235, 2019
202019
Preserving privacy between features in distributed estimation
C Heinze‐Deml, B McWilliams, N Meinshausen
stat 7 (1), e189, 2018
82018
CompareCausalNetworks: interface to diverse estimation methods of causal networks
C Heinze-Deml, N Meinshausen
R package, 2017
6*2017
Active invariant causal prediction: Experiment selection through stability
JL Gamella, C Heinze-Deml
arXiv preprint arXiv:2006.05690, 2020
32020
Latent Linear Adjustment Autoencoders v1. 0: A novel method for estimating and emulating dynamic precipitation at high resolution
C Heinze-Deml, S Sippel, AG Pendergrass, F Lehner, N Meinshausen
Geoscientific Model Development Discussions, 1-39, 2020
22020
Think before you act: A simple baseline for compositional generalization
C Heinze-Deml, D Bouchacourt
arXiv preprint arXiv:2009.13962, 2020
12020
Package ‘CondIndTests’
C Heinze-Deml, J Peters, AMS Munk, MC Heinze-Deml, T LazyData
2019
Computational Causality and Learning from Partitioned Data
C Heinze-Deml
ETH Zurich, 2018
2018
Uncovering the structure of complex data: progresses in machine learning and causal inference
M Besserve, C Heinze-Deml
Data Learning and Inference (DALI 2017), 2017
2017
Package ‘backShift’
C Heinze
https://cran.r-project.org/web/packages/backShift/backShift.pdf, 2015
2015
Causal Inference With Observational Data
V Lapteva, A Drewek, A Aigner, C Heinze, D Bürge, A Sokol
2013
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Articles 1–19