knowledge-structuring-variable-identification

Research

Identify key variables in the causal system

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Variable Identification

Systematically surface all variables that participate in the causal system under study. This strategy drives CC to read source material, extract candidate variables, create variable pages, and flag potential confounders before any mechanism edges are drawn.

Guiding Focus

CC must resist the urge to jump straight to causal edges. The quality of the entire causal model depends on having a complete, well-scoped variable inventory first. Every variable should be grounded in at least one source page, typed (exposure, outcome, mediator, moderator, confounder, or instrument), and given a plain-language description. Confounders deserve explicit flagging because they are the most common source of spurious causal claims.

Available Tactics

counterfactual-reasoning — use "what if this variable were absent?" to test whether a candidate is truly load-bearing
evidence-weighing — prioritize variables that appear across multiple independent sources over single-mention candidates

Budget Slice

Metric	S	M	L
Variables identified	6	15	30
Source pages analyzed	5	15	30
Confounders flagged	2	5	10

State Ledger Template

| Metric              | Target | Current | Status |
|---------------------|--------|---------|--------|
| Variables identified | S:6 / M:15 / L:30 | 0 | ⬜ |
| Source pages analyzed | S:5 / M:15 / L:30 | 0 | ⬜ |
| Confounders flagged | S:2 / M:5 / L:10 | 0 | ⬜ |

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
counterfactual-reasoning	Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis.
evidence-weighing	Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
variable-page-creation	SOP for creating a variable page in the causal model — documents a measurable quantity with its properties.