Graphical criteria for efficient total effect estimation via adjustment in causal linear models L Henckel, E Perković, MH Maathuis Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 93 | 2022 |
On efficient adjustment in causal graphs J Witte, L Henckel, MH Maathuis, V Didelez Journal of Machine Learning Research 21, 246, 2020 | 66 | 2020 |
Exploiting independent instruments: Identification and distribution generalization S Saengkyongam, L Henckel, N Pfister, J Peters International Conference on Machine Learning, 18935-18958, 2022 | 16 | 2022 |
pcalg: Methods for graphical models and causal inference M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ... R Package retrieved from https://CRAN. R-project. org/package= pcalg, 2021 | 11* | 2021 |
Graphical tools for selecting conditional instrumental sets L Henckel, M Buttenschoen, MH Maathuis Biometrika, asad066, 2023 | 5 | 2023 |
A robustness test for estimating total effects with covariate adjustment Z Su, L Henckel Uncertainty in Artificial Intelligence, 1886-1895, 2022 | 2 | 2022 |
Profiling Compliers in Instrumental Variables Designs D Hangartner, M Marbach, L Henckel, MH Maathuis, RR Kelz, L Keele arXiv preprint arXiv:2103.06328, 2021 | 2 | 2021 |
Graphical Tools for Efficient Causal Effect Estimation L Henckel ETH Zurich, 2021 | 1 | 2021 |
Faithlessness in Gaussian graphical models M Drton, L Henckel, B Hollering, P Misra arXiv preprint arXiv:2404.05306, 2024 | | 2024 |
Adjustment Identification Distance: A gadjid for Causal Structure Learning L Henckel, T Würtzen, S Weichwald arXiv preprint arXiv:2402.08616, 2024 | | 2024 |
A Graphical Approach to Treatment Effect Estimation with Observational Network Data ML Spohn, L Henckel, MH Maathuis arXiv preprint arXiv:2312.02717, 2023 | | 2023 |
Task-oriented performance metrics for structure learning L Henckel | | |