Causal Mapping
1 What’s in this guide?
I Introduction
2 Manifesto: piecing together fragments of causal information
3 Causal maps: a unifying idea
4 Causal mapping
5 The causal map app in four pictures
6 We need a “soft arithmetic” for causal maps
7 We need rules about how to encode causal information in a causal map (and decode it again)
- 7.1 How do causal claims work within ordinary language?
- 7.2 Understanding the elements of a causal map by agreeing how to make deductions with them
8 Soft arithmetic is also the answer to understanding causal maps
9 Metalanguage: some words for talking about causal maps
II The rules of Soft Arithmetic for causal maps
10 The mini-map coding rule
- 10.1 Meaning depends also on elicitation context
11 The mini-map coding rule – functional form
12 The extras rule: adding extra information, in particular about the levels or values of the variables
- 12.1 Interpretation
- 12.2 Corollary: Ordinary reasoning
13 The juxtaposition rule
14 Rules for joining maps
15 Rules for joining maps: what counts as “the same” variable?
16 The chaining rule, functional form (zooming out / black-box rule)
- 16.1 Two special cases
  - 16.1.1 Bare influence
  - 16.1.2 Total control
- 16.2 Technical note
17 The shared consequence rule, functional version
18 The shared influence rule, functional version
19 The shared arrow rule, functional form.
20 The rule for merging arrows. Weight of evidence.
- 20.1 Problems with evidence
21 The chaining rule with loops
22 The rule for conceptual links
- 22.1 Variations
- 22.2 Problems
23 Combining “extra” information like the values or levels of variables
24 Context
- 24.1 Intersections
- 24.2 Replacing contexts with variables
25 Maybe the nodes are not variables, maybe they are … propositions, events, schemas?
26 Variable-based and propositional-based approaches to causal diagrams: chalk and cheese??
27 Limitations to the proposition-based approach
28 Clarifying how to ask QuIP questions in contexts when the final outcomes are not necessarily “changes”
29 Coding individual causal fragments using propositions (revised)
30 Combining causal fragments from different sources using propositions
- 30.1 Same item
- 30.2 What these extensions still don’t do
  - 30.2.1 Can’t distinguish between AND and OR
  - 30.2.2 Necessary / sufficient conditions
31 Coding causal maps with propositions
32 Types of variable
33 Causal thinking is essentially contrastive thinking
34 Lo/hi Variables, types of variable, and contrasts
35 “For each ..….” variables
36 Coding specific influences – individually and in combination
37 One function to rule them all?
- 37.1 One universal type of function for coding causal claims with exclusively lo/hi variables.
38 Coding a claim about the absence of a causal link
39 Strength / importance
- 39.1 Including the idea of strength in our functional equation
- 39.2 Direct ways of eliciting how important is one variable’s contribution to another. (Fiona)
40 Causal Inference
- 40.1 Specifying the map
  - 40.1.1 Exogenous variables and the Grid
  - 40.1.2 Inference in action
41 Upstream (Bayesian) inference
42 Contour
- 42.1 Flipping
43 Valence
44 Valence and direction
45 Effect: two kinds
46 Interventions and differences
- 46.1 Shortcuts
47 Contribution
- 47.1 Why reporting correlation is not helpful
48 Information about the source of our causal information
- 48.1 Applying the “trust” attribute source-by-source
- 48.2 Including information about sources
49 Clusters of similar maps
50 Maps of maps
51 Probability density functions
III Simplification
52 Simplifying causal maps: aggregation and filtering
53 Aggregating and filtering beliefs
54 Aggregation and filtering based on face value
55 Aggregation and filtering based on particular research questions
56 Aggregation and filtering based on metrics
- 56.1 Network metrics
57 Citation intensity
58 Conspicuous absence
59 Not forwards
60 Homogenity of paths
IV Visualising
61 Visualising and formatting causal maps
62 Coding using the UI (outdated)
V Summary
63 Summary of the rules for inference in causal maps, aka “Soft Arithmetic”
VI The why question
64 The “why question” as a generic method in social research
- 64.1 Steps to asking “the why question”
- 64.2 When would you not want to use this approach?
VII The why question
65 The “why question” as a generic method in social research
- 65.1 Steps to asking “the why question”
- 65.2 When would you not want to use this approach?
66 The “why question” – asking about changes
- 66.1 “Changes”
VIII Using the causal map app
67 Using the causal map app – already partly outdated!
68 Using the causal map app for real-time, collaborative theory construction
IX Case studies
69 Case Study: the Strawberry Line
- 69.0.1 Theories gathered from activists
- 69.0.2 Stories gathered from the public
70 Case study: Global Young Academy. Tracing the paths of GYA’s impact
- 70.1 More details
71 Appendix: previous work
72 References
Published with bookdown

Causal Mapping

Section 57 Citation intensity

In QuIP, Citation Intensity is the citation count divided by the maximum possible citation count (no. Respondents x no. Domains).