most citedAdjustment Criteria in Causal Diagrams: An Algorithmic Perspective

43 citations · 61 across the 5 of their papers we have counts for

collaborators

5 papers

nlin.CG2023

Fitting Stochastic Lattice Models Using Approximate Gradients

Jan Schering, Sander Keemink, Johannes Textor

Stochastic lattice models (sLMs) are computational tools for simulating spatiotemporal dynamics in physics, computational biology, chemistry, ecology, and other fields. Despite the…

cs.NE2023

Implementing Immune Repertoire Models Using Weighted Finite State Machines

Gijs Schröder, Inge MN Wortel, Johannes Textor

The adaptive immune system's T and B cells can be viewed as large populations of simple, diverse classifiers. Artificial immune systems (AIS) $\unicode{x2013}$ algorithmic models o…

cs.LG202316 cited

pgmpy: A Python Toolkit for Bayesian Networks

Ankur Ankan, Johannes Textor

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to wo…

stat.ME20232 cited

Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models

Ankur Ankan, Inge Wortel, Kenneth A. Bollen +1

Measurement error is ubiquitous in many variables - from blood pressure recordings in physiology to intelligence measures in psychology. Structural equation models (SEMs) account f…

cs.AI201243 cited

Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective

Johannes Textor, Maciej Liskiewicz

Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be identi…