Inference and Causal Graphs: Potential Lessons from Qualitative Causal Inference

Directed acyclic graphs —causal graphs, for short— are increasingly recognized as a valuable element of causal inference. While they are primarily used for modeling relations of statistical dependence in quantitative approaches to inference, my own project over the past few years has been to adapt causal graphs to improve qualitative causal inference, a project culminating in my book manuscript, Qualitative Causal Inference & Explanation (Waldner 2024).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-10
DOI
https://doi.org/10.5281/zenodo.14062778
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Inference and Causal Graphs: Potential Lessons from Qualitative Causal Inference

Zenodo (CERN European Organization for Nuclear Research)
Bayesian Modeling and Causal Inference
article

Inference and Causal Graphs: Potential Lessons from Qualitative Causal Inference

article en

Abstract

Directed acyclic graphs —causal graphs, for short— are increasingly recognized as a valuable element of causal inference. While they are primarily used for modeling relations of statistical dependence in quantitative approaches to inference, my own project over the past few years has been to adapt causal graphs to improve qualitative causal inference, a project culminating in my book manuscript, Qualitative Causal Inference & Explanation (Waldner 2024).

Zenodo (CERN European Organization for Nuclear Research)
University of Virginia (US)
Openalex Percentile: Top 61%
Bayesian Modeling and Causal Inference
DOIOpen Access PDFOpenAlex
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Inference and Causal Graphs: Potential Lessons from Qualitative Causal Inference · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS