Extended causal diagram

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Extended causal diagrams are graphical representations of a particular situation, where the objects described are causally related to each other. In addition, the diagrams contain non-causal elements such as value judgements or inferences based on data. Extended causal diagrams utilise the ideas of directed acyclic graphs, but they have additional features.


What notation is simple and flexible enough so that it can be used to represent all major issues related to a policy situation? It must be usable in both graphical and data formats.


Legend for extended causal diagrams.svg



The parameters of an argument and possible combinations.
Id Title Content Sign Target Type Paradigm Relation Result Comment
arg1234 Short title for display Actual argument Signature arg9876 relevance science attack 1 If paradigm changes (all else equal), relation may change, although typically only the result changes.
arg1234 Short title for display Actual argument Signature arg5555 relevance science comment 0
arg1234 Short title for display Actual argument Signature arg6666 truth science defense 1 Truth refers to the truth of the target
arg1234 Short title for display Actual argument Signature arg1234 selftruth science attack 0 Selftruth refers to the truth of the argument itself, unlike other types that refer to the target.
arg1234 Short title for display Actual argument Signature arg9876 relevance toldya comment 0
arg1234 Short title for display Actual argument Signature arg5555 relevance toldya defense 1
arg1234 Short title for display Actual argument Signature arg6666 truth toldya attack 0
arg1234 Short title for display Actual argument Signature arg1234 selftruth toldya comment 1 The relation in case of type=selftruth is irrelevant and is ignored.
These are unique to an argument
These are unique to an argument-target pair
These are unique to a triple of argument-target-paradigm
Extended causal diagrams have been described in a scientific article manuscript From open assessment to shared understanding: practical experiences#Extended causal diagrams. Objects and their relations used in open policy practice are described on page Structure of shared understanding.

There is a need for methods facilitating the flow of information and understanding between science and policy. The principle is to describe a risk situation in a formal manner. Extended causal diagrams contain items along a causal pathway (or network) from e.g. abatement strategies to emissions to dispersion to exposure to effects. They have been designed to describe also non-causal connections such as non-causal reasoning, values, preferences, and arguments.

These diagrams use graph theory with vertices (or nodes) and arcs (or arrows). They are used to describe and define all the pieces needed for a description of the situation under scrutiny. Diagrams may be produced with any graphics software, providing that calculation functions are not required. If calculations are needed, we recommend the use of R software and OpasnetUtils package.

Parameter properties
Parameter Css selector (Opasnet page scraping) Requirements
Id .argument attr=id Must start with a letter
Title .argument .title Short text. Is shown on insight graph as node label
Content .argument .content Text, may be long. Is shown with hover on graph
Sign .argument .sign a:first-of-type Must contain a link to participant's user page. Is shown with hover on graph
Target NA Previous argument one level up, or the statement for arguments on the first level
Type .argument i.type One of the three: relevance, truth, or selftruth (or "both", which is depreciated)
Paradigm .argument .paradigm Each paradigm should be described on a dedicated page. The rules implemented must be clear
Relation .argument .relation Is one of these: attack, defense, comment. "Branches" are typically uninteresting and ignored.
  • relevance= .argument .relation attr=color. Gray= 0 (irrelevant), other=1 (relevant).
  • truth= .argument .relation attr=color. Gray=0 (untrue), other=1 (true)
  • selftruth= .argument .selftruth attr=color. Gray=0 (untrue), other=1 (true)
Truthlikeness of the relation. Either 1 or 0

This is the process how data flows into insight diagrams:

  • List of data tables of different insight diagrams is found from https://yhteistyotilat.fi/wiki08/x/1oGxAg. It has the following columns:
    • Ilmio: Name of the phenomenon. This will become the name of a csv data file.
    • Id: Identifier of the phenomenon. This will be used in Oldid of the items and relations.
    • Tyyppi: Type of the table. In practice, it defines the columns that the data table has. Different types are listed on #Types of insight network tables.
    • URL: Location of the data table. If the URL contains "google.com", it is assumed to be a google sheet. If the type (Tyyppi) is "keskustelu", it is assumed to be an Opasnet page with discussions. Otherwise, it is assumed to be a table on a web page that can be scraped with read_html() function.
    • Taulu: If the data is a table on a web page, it is the number of the table on that page. If the data is a discussion, it is the number of discussion; missing value means that all discussions on that page are read.
    • Alkurivi: In case of google sheets, it is the first row with actual data.
    • Kuvaus: Description of the table, with possible links to relevant description page.

All data tables and discussions are listed, formatted and saved as csv files in a zip file called op_fi:File:Näkemysverkkojen tietotauluja.zip. From there, the data can be accessed from within Opasnet Rtools. (The code scraping web pages does not work in Opasnet, although it is stored there.) Little formatting is done here, mainly the column titles are standardised. But the number and type of columns is not changed.

In the next phase, each csv file is opened, interpreted, and defined as items and relations. This is done in code Op_fi5810/graphs on page op_fi:Ympäristöterveysindikaattori. All these are saved as a DiagrammeR graph, and each topic may be separately selected as a subgraph.

  • ⇤--0094: . Are Tehtäväkokonaisuus, Osiotyyppi, JHS-luokka actually types of objects, or are they just indices. Yes, they should be indices and the objects relate to them with "has index". Correct table 4. (type: , paradigm: science view) --Jouni (talk) 20:46, 17 July 2018 (UTC)


Graphical properties of objects and relations

Graphical properties of objects and relations(-)
1defaultdefaultnode.shapecircleDefault values unless something else is specified
13defaultdefaultedge.fontsize10Not currently used
16defaultdefaultedge.penwidth2Not currently used
17defaultdefaultedge.arrowsize1Not currently used
18typeunknownnode.fillcolorYellowThis formatting is used if there are undefined objects
20typesubstancenode.shapecircleSubstantive type object
21typesubstancenode.fillcolorSkyBlue2Substantive type object
22typetasknode.shaperectangleDecision type object
23typetasknode.fillcolorRedDecision type object
24typeoptionnode.colorRedOption for a decision
25typeoptionnode.fillcolorWhiteOption for a decision
26typeindexnode.shapepolygonIndex or other classifying determinant
31typegraphnode.shapepolygonIndex or other classifying determinant
37typestakeholdernode.shapehexagonStakeholder type object
38typestakeholdernode.fillcolorYellowStakeholder type object
39typestakeholdernode.width0.8Stakeholder type object
40typemethodnode.shapeheptagonMethod type object
41typeprocessnode.shapepentagonProcess type object
42typeprocessnode.fillcolorPurpleProcess type object
43typedatanode.shaperectangleData type object
44typedatanode.fillcolorOrangeData type object
45typeobjectivenode.shapediamondObjective type object
46typeobjectivenode.fillcolorYellowObjective type object
47typeobjectivenode.width0.8Objective type object
48typepublicationnode.fillcolorGrayPublication type object
49typeargumentnode.shapepolygonArgument type object
50typeargumentnode.sides4Argument type object
51typeargumentnode.width0.8Argument type object
52typeargumentnode.distortion-0.5Argument type object
53typetrue argumentnode.fillcolorOrangeArgument type object
54typefalse argumentnode.fillcolorGrayArgument type object
55typestatementnode.fillcolorPinkArgument type object
56typefact discussionnode.fillcolorBlueArgument type object
57typevalue discussionnode.fillcolorGreenValue judgement type object
58typerisk factornode.colorPinkAdditional information about object class
59typeindicatornode.colorBrownAdditional information about object class
60typeindicatornode.fillcolorOrangeAdditional information about object class
61typetasknode.colorGreenAdditional information about object class
62typedatanode.colorOrangeAdditional information about object class
63typehealth organisationnode.colorYellowAdditional information about object class
64Relationcausal linkedge.colorBlackCausal link
65Relationcausal linkedge.stylesolidCausal link
66Relationpositive causal linkedge.fontcolorGreenCausal link
67Relationnegative causal linkedge.fontcolorRedCausal link
68Relationparticipatory linkedge.colorPurpleParticipatory link
69Relationparticipatory linkedge.styledashedParticipatory link
70Relationoperational linkedge.colorBlackOperational link
71Relationoperational linkedge.styledashedOperational link
72Relationevaluative linkedge.colorGreenEvaluative link
73Relationrelevant attackedge.colorRedAttacking argument
74Relationrelevant defenseedge.colorGreenDefending argument
75Relationrelevant commentedge.colorBlueCommenting argument
76Relationirrelevant argumentedge.colorGrayInvalid argument
77Relationargumentative linkedge.styledottedArgumentative link
78Relationargumentative linkedge.penwidth4Argumentative link
79Relationreferential linkedge.colorRedReferential link
80Relationreferential linkedge.styledashedReferential link

Types of insight network tables

Table types(-)
ObsTypeColumn namesDummy
1oletusLuokka, Asia, Lyhenne, Relaatio, Kohde, Kuvaus, URL1
2sotearvIkaryhma, AHVK, Nimi, Tehtavakokonaisuus, Ulottuvuus, Osiotyyppi, Tietolahde, KUVA id, Sotkanet id, JHS-luokka, Ryhman perustelut1
3hnh2035Teema, Nro, Toimenpide, Ohjelma, Vastuu, Aikajanne, Vaativuus, Kustannukset, Kust.kaupungille, Hyodyt.kaupungille, Kust.muille, Hyodyt.muille, Paastovahenema, Muut.vaikutukset, Seurantaindikaattori, Esimerkki, Lisatietoa1
4kuvaindTunnistenumero, Nimi, Aihe1, Aihe2, Aihe3, Tietopaketti1, Tietopaketti2, Tietopaketti3, Mita.mittaa, Mitta-arvo ja muodostaminen, Tietolahde, Tietolahde ja tausta, Tuotannossa, Tietotarpeen taso, Tuottamistaso, Kansainvaliset tietotoimitukset, Muut kayttotarkoitukset, Lisatietoa1
5sitra100Nimi, Suuruus, Teema, URL1
6hvkertomusNro, Nimi, Teema, Vaestoryhma, Lahde ja muodostaminen, Mita mittaa, KUVA-mittarissa, Hyte-kertoimessa, Arviointiraportissa, Hyvinvointikertomuksessa, Perustelut1
7hnhosNro, Luokka, Asia, Kuvaus1
8keskusteluNro, Luokka, Asia, Lyhenne, Relaatio, Kohde, Kuvaus, URL1


Test code for DiagrammeR diagrams in Opasnet

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Collect and modify insight networks into a zip repository


Making insight networks: ecd_create

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Discograph function

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Assessment networks: odag

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Copy descriptions to ovariables

The function assessmentDescriptions scans through an assessment ovarible that has all relevant assessment objects as parents. Dependencies slot may also have additional information, such as the following.

  • Name: name of parent (obligatory)
  • Ident: Opasnet page identifier and code name where the parent object can be loaded (e.g. Op_en7748/hia). Note: This is typically the code for the whole assessment, not the individual codes for the objects.
  • Token: Token for the model run where the parent object can be loaded (e.g. xxNsLw5hWdM6xyYp)
  • Description: A short description about what the object is. This is typically shown when cursor hovers over the object on an online insight diagram.
  • Page: Opasnet page identifier for the object's knowledge crystal page, which contains the research question, answer, and description of the object, together with discussion, if any. Typically this is empty for ovariables, because this information can be found from ovariable@meta slot and there is no need to duplicate it here.
  • Child: An object to which this object links. This is typically needed for objects such as graphs and data.frames that do not contain this information in their own structure, unlike ovariables. The direction of a relation is away from this object because then this object is the subject in triple sentences and can be given other parameters as well in other columns. A typical sentence is "graph describes ovariable", but for illustrative purposes this is inversed on insight networks so that the arrow points from an ovariable to a graph ("ovariable is described by graph").
  • Other columns are allowed.

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Old notation

⇤--#: . Look at the table below together with Structure of shared understanding and merge. Decide which things should be on this page and which should be on the other. (type: truth, paradigm: science view) --Jouni (talk) 06:55, 24 April 2018 (UTC)

Node type Object Colour code in Analytica Comments
General variable.png General variable 8R3B (automatic) This is a deterministic function of the quantities it depends on.
Chance variable.png Chance variable 11L4B (autom) This is a variable which is uncertain and uncontrollable (in a direct sense).
Data-driven variable.png Data-driven variable 3R1B A general variable where the result is mostly driven by data (observations or literature).
Author judgement variable.png Author judgement variable 4R2B A general variable where the result is mainly driven by author judgement (estimates with poor or no data).
Decision variable.png Decision variable 9L3B This is the variable that a decision-maker has the power to control. The decision variable should always be at the top of the chain of causality, even if this is a subchain i.e. it should not have any parent variables. Essentially the decision variable should be regarded as a decision that has to be made; since many factors affect all decisions it is not (in the case of INTARESE) an efficient use of resources to attempt to model what leads a decision-maker to make his decision.
Objective variable.png Indicator 1R3B (autom) This is a variable of special interest. One of the indicators in an assessment may be the quantitative criterion that you are trying to optimize.

A particularly important variable in relation to the interests of the intended users of the assessment output (i.e. it must be a means of effective communication of assessment results).

  • It must be in causal connection to the endpoints of the assessment and thus address causality throughout the full chain.
  • It should reflect the use/purpose of the assessment.
  • It should address and be adapted according to the target audience.
  • It should be the ‘leading component’ in the assessment process.
Value judgement variable.png Value judgement variable 8L4B A preference or value that a person or a group assigns to a particular condition or state of the world.
Index variable.png Index (or dimension) 5R2B (autom) This identifies the dimensions of the variable to which it is linked. Note that these dimensions do not have to be numeric, but can also be classes etc.
Risk assessment node.png Risk assessment 8R3B (autom)
Scope node.png Scope 6R1B The scope of the object
Conclusion node.png Conclusion 6L3B A conclusion of the risk assessment (Result/Conclusion attribute).
Module node.png Module 6R3B (autom) A group of variables that are put together for illustrative or other practical reasons.
Proxy variable.png Data 2L3B (autom) Contents of the Definition/Data attribute of a variable. If the Result attribute of a variable is used as Data for another variable, the first variable is called a proxy, and this node is used in the diagram. If an arrow or line is drawn between these objects, it must be noticed that this is NOT a causal link but an inference link. The direction of the arrow would be from the proxy to the variable.
Argument node.png Argument 8R2B A piece of argumentation related to an object (variable, risk assessment, or class)
Formula node.png Formula 9L3B Contents of the Definition/Formula attribute of a variable.
Class node.png Class 1L2B A class object (a set of objects that share a particular property).
Function node.png Function 4R2B (autom) A special kind of class. The particular property that is shared contains a full description of the Scope and the Definition attributes with given parameters.
Causal arrow.png Causal arrow This states a causal relationship (or influence) of one variable onto another. Note that causal arrows can only exist between two arrows; any arrows to or from non-causal objects are non-causal inference arrows.
Non-causal arrow.png Non-causal arrow This states an inference relationship between two objects. This means that the object where the arrow starts from is in the Data attribute of the other object. It is thus used to infer something about the value of the result of the latter object. Either object can be a variable or a non-variable. Note that Analytica is only able to show one kind of arrows, so in some cases the nature of the arrow (causal or inference) must be concluded from the context.

Previous notations

Previous notation for extended causal diagrams. This version was optimised for Analytica use.

Extended causal diagrams have previously been called pyrkilo diagrams and factor-effect-value networks. These names are no longer in active use. An archived version of the notation can be found from an earlier version of this page.

See also