Published papers:
L. Henckel, M. Buttenschon, M. H. Maathuis Graphical tools for selecting conditional instrumental sets. Biometrika, to appear. link

S. Zehao, 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

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

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 R-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:
D. Hangartner, M. Marbach, L. Henckel, M. H. Maathuis, L. Keele. Profiling compliers in instrumental variable designs. arxiv

M. L. Spohn, L. Henckel, M. H. Maathuis. A Graphical Approach to Treatment Effect Estimation with Observational Network Data. arxiv


Software:
Contributing author to the R-package pcalg


Thesis:
Graphical Tools for Efficient Causal Effect Estimation ETH research collection