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Dominik Rothenhäusler
Dominik Rothenhäusler
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Anchor regression: Heterogeneous data meet causality
D Rothenhäusler, N Meinshausen, P Bühlmann, J Peters
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021
2132021
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
D Rothenhäusler, C Heinze, J Peters, N Meinshausen
advances in neural information processing systems 28, 2015
812015
Causal Dantzig
D Rothenhäusler, P Bühlmann, N Meinshausen
The Annals of Statistics 47 (3), 1688-1722, 2019
462019
Robust factor selection in early cell culture process development for the production of a biosimilar monoclonal antibody
M Sokolov, J Ritscher, N MacKinnon, JM Bielser, D Brühlmann, ...
Biotechnology progress 33 (1), 181-191, 2017
392017
Guilt in voting and public good games
D Rothenhäusler, N Schweizer, N Szech
European Economic Review 101, 664-681, 2018
362018
Causal inference in partially linear structural equation models
D Rothenhäusler, J Ernest, P Bühlmann
The Annals of Statistics 46 (6A), 2904-2938, 2018
262018
Incremental causal effects
D Rothenhäusler, B Yu
arXiv preprint arXiv:1907.13258, 2019
182019
Confidence intervals for maximin effects in inhomogeneous large-scale data
D Rothenhäusler, N Meinshausen, P Bühlmann
Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014, 255-277, 2016
152016
The s-value: evaluating stability with respect to distributional shifts
S Gupta, D Rothenhäusler
Advances in Neural Information Processing Systems 36, 2024
142024
Institutions, shared guilt, and moral transgression
D Rothenhäusler, N Schweizer, N Szech
CESifo Working Paper Series, 2015
142015
Distributionally robust and generalizable inference
D Rothenhäusler, P Bühlmann
Statistical Science 38 (4), 527-542, 2023
112023
Causal inference in partially linear structural equation models: identifiability and estimation
J Ernest, D Rothenhäusler, P Bühlmann
arXiv preprint arXiv:1607.05980, 2016
112016
Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty
Y Jeong, D Rothenhäusler
arXiv preprint arXiv:2202.11886, 2022
82022
On the statistical role of inexact matching in observational studies
K Guo, D Rothenhäusler
Biometrika 110 (3), 631-644, 2023
62023
Bin Yu. Incremental causal effects
D Rothenhäusler
arXiv preprint arXiv:1907.13258, 2019
52019
Learning under random distributional shifts
KC Bansak, E Paulson, D Rothenhäusler
International Conference on Artificial Intelligence and Statistics, 3943-3951, 2024
32024
Tailored inference for finite populations: conditional validity and transfer across distributions
Y Jin, D Rothenhäusler
Biometrika 111 (1), 215-233, 2024
32024
Diagnosing the role of observable distribution shift in scientific replications
Y Jin, K Guo, D Rothenhäusler
arXiv preprint arXiv:2309.01056, 2023
22023
Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
JR Gimenez, D Rothenhäusler
Journal of Machine Learning Research 23 (335), 1-60, 2022
22022
Attribute-adaptive statistical inference for finite populations under distribution shift
Y Jin, D Rothenhäusler
arXiv preprint arXiv:2104.04565, 2021
22021
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