5 papers
Automated Hyperparameter Selection for the PC Algorithm
Eric V. Strobl
The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I level. PC is however unsupervised, so we cannot tune using…
The Global Markov Property for a Mixture of DAGs
Eric V. Strobl
Real causal processes may contain feedback loops and change over time. In this paper, we model cycles and non-stationary distributions using a mixture of directed acyclic graphs (D…
Causal Discovery with a Mixture of DAGs
Eric V. Strobl
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose t…
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Eric V. Strobl
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm ca…
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before…