Co-Developing Causal Graphs with Domain Experts Guided by Weighted FDR-Adjusted p-values
This paper proposes an approach facilitating co-design of causal graphs between subject matter experts and statistical modellers. Modern causal analysis starting with formulation of causal graphs provides benefits for robust analysis and well-grounded decision support. Moreover, this process can enr...
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Format | Journal Article |
Language | English |
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04.09.2024
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Abstract | This paper proposes an approach facilitating co-design of causal graphs
between subject matter experts and statistical modellers. Modern causal
analysis starting with formulation of causal graphs provides benefits for
robust analysis and well-grounded decision support. Moreover, this process can
enrich the discovery and planning phase of data science projects.
The key premise is that plotting relevant statistical information on a causal
graph structure can facilitate an intuitive discussion between domain experts
and modellers. Furthermore, Hand-crafting causality graphs, integrating human
expertise with robust statistical methodology, enables ensuring responsible AI
practices.
The paper focuses on using multiplicity-adjusted p-values, controlling for
the false discovery rate (FDR), as an aid for co-designing the graph. A family
of hypotheses relevant to causal graph construction is identified, including
assessing correlation strengths, directions of causal effects, and how well an
estimated structural causal model induces the observed covariance structure.
An iterative flow is described where an initial causal graph is drafted based
on expert beliefs about likely causal relationships. The subject matter
expert's beliefs, communicated as ranked scores could be incorporated into the
control of the measure proposed by Benjamini and Kling, the FDCR (False
Discovery Cost Rate). The FDCR-adjusted p-values then provide feedback on which
parts of the graph are supported or contradicted by the data. This co-design
process continues, adding, removing, or revising arcs in the graph, until the
expert and modeller converge on a satisfactory causal structure grounded in
both domain knowledge and data evidence. |
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AbstractList | This paper proposes an approach facilitating co-design of causal graphs
between subject matter experts and statistical modellers. Modern causal
analysis starting with formulation of causal graphs provides benefits for
robust analysis and well-grounded decision support. Moreover, this process can
enrich the discovery and planning phase of data science projects.
The key premise is that plotting relevant statistical information on a causal
graph structure can facilitate an intuitive discussion between domain experts
and modellers. Furthermore, Hand-crafting causality graphs, integrating human
expertise with robust statistical methodology, enables ensuring responsible AI
practices.
The paper focuses on using multiplicity-adjusted p-values, controlling for
the false discovery rate (FDR), as an aid for co-designing the graph. A family
of hypotheses relevant to causal graph construction is identified, including
assessing correlation strengths, directions of causal effects, and how well an
estimated structural causal model induces the observed covariance structure.
An iterative flow is described where an initial causal graph is drafted based
on expert beliefs about likely causal relationships. The subject matter
expert's beliefs, communicated as ranked scores could be incorporated into the
control of the measure proposed by Benjamini and Kling, the FDCR (False
Discovery Cost Rate). The FDCR-adjusted p-values then provide feedback on which
parts of the graph are supported or contradicted by the data. This co-design
process continues, adding, removing, or revising arcs in the graph, until the
expert and modeller converge on a satisfactory causal structure grounded in
both domain knowledge and data evidence. |
Author | Kling, Eli Y |
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BackLink | https://doi.org/10.48550/arXiv.2409.03126$$DView paper in arXiv |
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Snippet | This paper proposes an approach facilitating co-design of causal graphs
between subject matter experts and statistical modellers. Modern causal
analysis... |
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SubjectTerms | Statistics - Methodology |
Title | Co-Developing Causal Graphs with Domain Experts Guided by Weighted FDR-Adjusted p-values |
URI | https://arxiv.org/abs/2409.03126 |
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