Published papers:
L. Henckel, T. Wurtzen, S. Weichwald
Adjustment Identification Distance: A gadjid for Causal Structure Learning.
Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 244:1569-1598, 2024.
link
code
L. Henckel, M. Buttenschon, M. H. Maathuis Graphical tools for selecting conditional instrumental sets. Biometrika, 771-788, 2024. link
Z. Su, L. Henckel. A Robustness Test for Estimating Total Effects with Covariate Adjustments. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 180:1886-1895, 2022. link code
S. Saengkyongam, L. Henckel, N. Pfister, J. Peters. Exploiting Independent Instruments: Identification and Distribution Generalization. Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR 162:18935-18958, 2022. link code
L. Henckel, E. Perkovi'c, M. H. Maathuis. Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models. Journal of the Royal Statistical Society: Series B, 84:579-599, 2022. link code
J. Witte, L. Henckel, M. H. Maathuis, V. Didelez. On efficient adjustment in causal graphs. Journal of Machine Learning Research, 21(246):1-45, 2020. link
Working papers:
M. Drton, L. Henckel, B. Hollering, P. Misra.
Faithlessness in Gaussian graphical models.
arxiv
M. L. Spohn, L. Henckel, M. H. Maathuis. A Graphical Approach to Treatment Effect Estimation with Observational Network Data. arxiv
D. Hangartner, M. Marbach, L. Henckel, M. H. Maathuis, L. Keele. Profiling compliers in instrumental variable designs. arxiv code
Software:
Contributing author to the R-package pcalg
Author for the Python-package gadjid
Thesis:
Graphical Tools for Efficient Causal Effect Estimation ETH research collection