62 citations · 104 across the 14 of their papers we have counts for
14 papers
Verifying formulas for interventional distributions
Francesco Freni, Leonard Henckel, Sebastian Weichwald
We formalize verification in causal graphical models: deciding whether a given observational formula identifies a target interventional distribution. This opens a problem complemen…
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…
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
Marcel Wienöbst, Leonard Henckel, Sebastian Weichwald
We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score…
Linear-Time Primitives for Algorithm Development in Graphical Causal Inference
Marcel Wienöbst, Sebastian Weichwald, Leonard Henckel
We introduce CIfly, a framework for efficient algorithmic primitives in graphical causal inference that isolates reachability as a reusable core operation. It builds on the insight…
Causal inference amid missingness-specific independencies and mechanism shifts
Johan de Aguas, Leonard Henckel, Johan Pensar +1
The recovery of causal effects in structural models with missing data often relies on -graphs, which assume that missingness mechanisms do not directly influence substantive var…
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…
Adjustment Identification Distance: A gadjid for Causal Structure Learning
Leonard Henckel, Theo Würtzen, Sebastian Weichwald
Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the…
A Graphical Approach to Treatment Effect Estimation with Observational Network Data
Meta-Lina Spohn, Leonard Henckel, Marloes H. Maathuis
We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactio…
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…
A Robustness Test for Estimating Total Effects with Covariate Adjustment
Zehao Su, Leonard Henckel
Suppose we want to estimate a total effect with covariate adjustment in a linear structural equation model. We have a causal graph to decide what covariates to adjust for, but are…
Exploiting Independent Instruments: Identification and Distribution Generalization
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister +1
Instrumental variable models allow us to identify a causal function between covariates and a response , even in the presence of unobserved confounding. Most of the existing…
Profiling Compliers in Instrumental Variables Designs
Dominik Hangartner, Moritz Marbach, Leonard Henckel +3
Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. IV analyses can be used both to analyze randomized trials with noncompliance a…
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…
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…