This chapter is a set of working papers about causal mapping as qualitative evidence management: we code reported causal influence claims from text as a links table with provenance, then analyse the resulting evidence base through explicit transforms and queries.
The overall aim is to keep the core representation minimal and auditable, while still supporting powerful downstream analysis (filter pipelines, standardisation/recoding, coverage/fit diagnostics), including workflows that use LLMs as low-level assistants for extraction and labelling.
Core papers (start here)#
- Minimalist coding for causal mapping: the core coding stance (“barefoot” link coding), why it is useful, and where it breaks.
- A formalisation of causal mapping: companion spec—data structures + conservative rules for aggregation/query.
- Causal mapping as causal QDA: positioning for qualitative methods / CAQDAS audiences.
Practical extensions (operations on a links table)#
- Magnetisation: soft recoding with “magnets” (standardise labels at scale without re-coding quotes).
- A simple measure of the goodness of fit of a causal theory to a text corpus: coverage-style diagnostics for ToC fit.
- Combining opposites, sentiment and despite-claims: opposites transforms, sentiment as an annotation layer, and “despite” link typing.
- Hierarchical coding: hierarchical labels (
;) and zoom-style simplification.
Related notes / fragments / examples#
- !!!Qualitative Split-Apply-Combine: small‑Q framing; causal mapping as a SAC variant; where genAI fits.
- 250! causal mapping turns QDA on its head: a short argument/fragment (kept for reuse).
- Conversational AI — Analysing Central Bank speeches: worked example of “clerk vs architect” (auto-extraction + magnet-style structuring).
Intended audience: evaluators / applied qualitative researchers who want a teachable causal coding protocol, and AI/NLP readers who want a simple, auditable target representation of causal content in text.
Instead we take a piece-by-piece approach:
Unique contribution (what this paper adds):
See also: [[000 Working Papers ((working-papers))]]; [[005 Minimalist coding for causal mapping]]; [[900 Magnetisation]].
Intended audience: people who have done open-ended (often in‑vivo) causal coding and need to standardise factor vocabularies for readable maps/tables without destroying provenance.
See also: [[000 Working Papers ((working-papers))]]; [[005 Minimalist coding for causal mapping]]; [[900 Magnetisation]]; [[040 Causal mapping as causal QDA]].