J. Textor

发表

Local fit evaluation of structural equation models using graphical criteria.

Y. Rosseel, F. Thoemmes, J. Textor, 2017, Psychological methods.

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

J. Textor, Ankur Ankan, 2022, ArXiv.

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

J. Textor, Ankur Ankan, 2022, AAAI.

Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs

Johannes Textor, Marloes H. Maathuis, Markus Kalisch, 2016, J. Mach. Learn. Res..

DAGitty: a graphical tool for analyzing causal diagrams.

Johannes Textor, Juliane Hardt, Sven Knüppel, 2011, Epidemiology.

Robust causal inference using Directed Acyclic Graphs: the R package

Maciej Liskiewicz, Johannes Textor, Mark S Gilthorpe, 2018 .

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

Kellyn F Arnold, Wendy J Harrison, Georgia D Tomova, 2020, International journal of epidemiology.

A generic finite automata based approach to implementing lymphocyte repertoire models

Maciej Liskiewicz, Johannes Textor, Katharina Dannenberg, 2014, GECCO.

Search and Learning in the Immune System: Models of Immune Surveillance and Negative Selection

Johannes Textor, J. Textor, 2013, it Inf. Technol..

Efficient Negative Selection Algorithms by Sampling and Approximate Counting

Johannes Textor, J. Textor, 2012, PPSN.

Efficiently Finding Conditional Instruments for Causal Inference

Maciej Liskiewicz, Johannes Textor, Benito van der Zander, 2015, IJCAI.

Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective pdf

Maciej Liskiewicz, Johannes Textor, J. Textor, 2011, UAI.

Inverse Square Lévy Walks are not Optimal Search Strategies for d≥2.

Johannes Textor, Olivier Bénichou, Raphaël Voituriez, 2020, Physical review letters.

Testing Graphical Causal Models Using the R Package “dagitty”

Inge M. N. Wortel, J. Textor, Ankur Ankan, 2021, Current protocols.

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

M. Gilthorpe, J. Textor, G. Ellison, 2018, Statistical methods in medical research.

Negative selection algorithms on strings with efficient training and linear-time classification

Michael Elberfeld, Johannes Textor, Michael Elberfeld, 2011, Theor. Comput. Sci..

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

Inge M. N. Wortel, K. Bollen, J. Textor, 2023, 2302.13220.

Drawing and Analyzing Causal DAGs with DAGitty pdf

Johannes Textor, J. Textor, 2015, ArXiv.

Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

Kellyn F Arnold, Wendy J Harrison, Claire Keeble, 2019 .

Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework

Maciej Liskiewicz, Johannes Textor, Benito van der Zander, 2018, Artif. Intell..

pgmpy: A Python Toolkit for Bayesian Networks

J. Textor, Ankur Ankan, 2023, ArXiv.

HYBRID SIMULATION ALGORITHMS FOR AN AGENT-BASED MODEL OF THE IMMUNE RESPONSE

Johannes Textor, Björn Hansen, J. Textor, 2009, Cybern. Syst..

A Complete Generalized Adjustment Criterion

Johannes Textor, Marloes H. Maathuis, Markus Kalisch, 2015, UAI.

Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

G. Ellison, M. Gilthorpe, J. Textor, 2017, International journal of epidemiology.

A Comparative Study of Negative Selection Based Anomaly Detection in Sequence Data

Johannes Textor, J. Textor, 2012, ICARIS.

DAGitty: a graphical tool for analyzing causal diagrams.

J. Textor, S. Knüppel, J. Hardt, 2011, Epidemiology.

Constructing Separators and Adjustment Sets in Ancestral Graphs

Maciej Liskiewicz, Johannes Textor, Benito van der Zander, 2014, CI@UAI.

Learning from Pairwise Marginal Independencies

Maciej Liskiewicz, Johannes Textor, Alexander Idelberger, 2015, UAI.

[Our moral ancestors: determining adjustment sets in causal diagrams with ease].

J. Textor, 2011, Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)).

Stochastic Search with Locally Clustered Targets: Learning from T Cells

Rüdiger Reischuk, Johannes Textor, R. Reischuk, 2011, ICARIS.

Negative selection algorithms without generating detectors

Maciej Liskiewicz, Johannes Textor, J. Textor, 2010, GECCO '10.

Efficient Algorithms for String-Based Negative Selection

Michael Elberfeld, Johannes Textor, Michael Elberfeld, 2009, ICARIS.