55 citations · 66 across the 17 of their papers we have counts for
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stat.ME2026
COMPACT: Spectral Adjustment Scores from a Complete and Irreducible Causal Criterion
Eric V. Strobl
Observational datasets frequently contain many baseline variables, yet investigators estimating causal effects may not know which variables to include in the adjustment set. Confou…
stat.ME2017
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…
stat.ME2017
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Eric V. Strobl, Kun Zhang, Shyam Visweswaran
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one…