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20192026
most citedGraphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models

62 citations · 104 across the 14 of their papers we have counts for

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math.ST2025

Model-oriented Graph Distances via Partially Ordered Sets

Armeen Taeb, F. Richard Guo, Leonard Henckel

A well-defined distance on the parameter space is key to evaluating estimators, ensuring consistency, and building confidence sets. While there are typically standard distances to…

math.ST2024

Faithlessness in Gaussian graphical models

Mathias Drton, Leonard Henckel, Benjamin Hollering +1

The implication problem for conditional independence (CI) asks whether the fact that a probability distribution obeys a given finite set of CI relations implies that a further CI s…

math.ST2022★ 7 cited

Graphical tools for selecting conditional instrumental sets

Leonard Henckel, Martin Buttenschön, Marloes H. Maathuis

We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage…

math.ST2020★ 27 cited

On efficient adjustment in causal graphs

Janine Witte, Leonard Henckel, Marloes H. Maathuis +1

We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting kn…

math.ST2019★ 62 cited

Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models

Leonard Henckel, Emilija Perković, Marloes H. Maathuis

Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, th…