Lourens Waldorp | Causal discovery | Innovative Research Award

Published on by Cognitive Scientist Awards

Innovative Research Award

Lourens Waldorp
University of Amsterdam, Netherlands

Lourens Waldorp
Affiliation University of Amsterdam
Country Netherlands
Scopus ID 6507097986
Documents 123
Citations 16,925
h-index 37
Subject Area Causal discovery
Event International Cognitive Scientists Award
ORCID 0000-0002-5941-4625

Lourens Waldorp is an associate professor in the Department of Psychological Methods at the University of Amsterdam whose research spans network analysis, graphical models, high-dimensional statistics, dynamical systems and causality. His work connects mathematical and statistical methods with psychological and biophysical applications, with particular attention to network discovery and causal relationships. [1]

Abstract

This academic recognition profile presents Lourens Waldorp in the context of the Innovative Research Award associated with the International Cognitive Scientists Award. Waldorp’s research focuses on statistical and computational approaches to networks, causality, graphical models and dynamic systems. His work has contributed to methodological discussions surrounding causal inference, network modelling and the analysis of complex psychological data. [1][2]

Keywords

Causal discovery, causal inference, graphical models, network analysis, high-dimensional statistics, psychological methods, dynamical systems, network discovery, mathematical statistics, cognitive science.

Introduction

Causal discovery is an important methodological area for understanding relationships among variables and distinguishing statistical association from plausible causal structure. Waldorp’s research addresses this area through mathematical modelling, graph-based approaches and statistical inference. The University of Amsterdam identifies his expertise in graph theory, graphical models, network discovery, mathematical statistics, high-dimensional statistics, dynamical systems and causality. [1]

Research Profile

Waldorp’s research profile combines statistical theory with applications in psychology and related quantitative sciences. His publications address network inference, causal relations, time-series models, graphical models and statistical properties of complex systems. This interdisciplinary orientation provides a methodological basis for analysing multivariate and longitudinal data. [1][3]

Research Contributions

Among his contributions are studies of causal inference, perturbation graphs and invariant causal prediction. His work with Kossakowski and van der Maas examined approaches to causal inference graphs, while later research considered principles intended to unify experimental and observational accounts of causal inference. [2][4] These contributions are relevant to researchers seeking rigorous approaches for identifying and evaluating causal structures in complex datasets.

Publications

Selected publications illustrate the breadth of Waldorp’s methodological work. These include research on network inference with the lasso, perturbation graphs and causal relations, causal inference principles, network analysis in psychological science, and comparisons of techniques for causal inference graphs. [1][3][4]

  • Waldorp, L., Kossakowski, J., & van der Maas, H. L. J. — Perturbation graphs, invariant causal prediction and causal relations in psychology. [4]
  • Waldorp, L., Schutzeichel, F., & de Jong, P. — Causal inference: Principles unifying experimental and observational accounts. [2]
  • Waldorp, L. & Haslbeck, J. — Network Inference with the Lasso. [3]

Research Impact

The supplied research metrics record 123 documents, 16,925 citations and an h-index of 37. These figures indicate substantial bibliometric visibility, although citation metrics should be interpreted alongside publication quality, methodological significance and disciplinary context. The University of Amsterdam profile also records a substantial body of refereed publications across network analysis, statistics, causality and related fields. [1]

Award Suitability

Based on the supplied profile, Waldorp’s research in causal discovery and related statistical methodologies provides a clear scholarly basis for consideration for an innovative research recognition. His work combines theoretical development with applications to psychological and complex-system data, while his publication record demonstrates sustained engagement with methodological research. [1][2]

Conclusion

Lourens Waldorp’s research represents an interdisciplinary contribution to causal discovery, network analysis and statistical methodology. His work on causal inference, graphical models and dynamic systems supports the development of quantitative approaches for understanding complex relationships in psychological and related scientific data. [1][2][3]

References

  1. University of Amsterdam. Dr. L.J. (Lourens) Waldorp. Faculty of Social and Behavioural Sciences, Programme Group Psychological Methods.

    Official profile. [1]

  2. Waldorp, L., Schutzeichel, F., & de Jong, P. J. (2025). Causal inference: Principles unifying experimental and observational accounts. Theory and Psychology, 35(5), 575–599. DOI:

    https://doi.org/10.1177/09593543251325506. [2]

  3. Waldorp, L., & Haslbeck, J. (2024). Network Inference with the Lasso. Multivariate Behavioral Research, 59(4), 738–757. DOI:

    https://doi.org/10.1080/00273171.2024.2317928. [3]

  4. Waldorp, L., Kossakowski, J., & van der Maas, H. L. J. (2025). Perturbation graphs, invariant causal prediction and causal relations in psychology. British Journal of Mathematical and Statistical Psychology, 78(1), 303–340. DOI:

    https://doi.org/10.1111/bmsp.12361. [4]