3 papers
math.ST2025
Selecting valid adjustment sets with uncertain causal graphs
Zhongyi Hu, Stéphanie van der Pas
Precise knowledge of causal directed acyclic graphs (DAGs) is assumed for standard approaches towards valid adjustment set selection for unbiased estimation, but in practice, the D…
stat.ME2025
Bayesian regression discontinuity design with unknown cutoff
Julia Kowalska, Mark van de Wiel, Stéphanie van der Pas
The regression discontinuity design (RDD) is a quasi-experimental approach used to estimate the causal effects of an intervention assigned based on a cutoff criterion. RDD exploits…
math.ST2025
Finite sample-optimal adjustment sets in linear Gaussian causal models
Nadja Rutsch, Sara Magliacane, Stéphanie van der Pas
Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causa…