Some of the organisations who have used Causal Map:
Some of the organisations for which BathSDR has conducted evaluations using Causal Map:
Most reports written using Causal Map are unfortunately not available publicly. Here are a few which are.
Here is a brief presentation showcasing a recent project conducted by Bath SDR for VOSCUR using Causal Map.
VOSCUR are a Bristol-based charity who support organisations in the voluntary, community and social enterprise (VCSE) sector.
Causal Map was used to create maps like below, which shows the influences and consequences of access to increased funding. This map shows links which were mentioned at least twice.
Click here to read the full report.
The Causal Map App contributed to the evaluation of the World Food Programme (WFP) market development activities and related food systems support activities in Southern Africa from 2018 to 2021. Data from key informant interviews and focus group discussions with retailers in four countries (Lesotho, Malawi, Mozambique, and Zimbabwe) were coded and mapped out using Causal Map to illustrate trends, anomalies, and possible entry points. The WFP used maps and table from the app to clearly visualise the research findings in their report.
Here is a brief presentation showcasing a recent project conducted by Bath SDR for Power to Change using Causal Map.
Path tracing was a useful tool when drawing out stories of change.
Oct-Dec 2018. Impact of academic support, Anamaria Golemac Powell & Steve Powell.
This report used a very early version of Causal Map.
The Global Young Academy supports young scientists around the world to connect with other young scientists, develop their careers and work towards solving global problems with science. We asked over 100 people, who had been supported by GYA or were otherwise involved in the programme, to tell us stories about positive changes which had happened because of GYA activities. We used an online survey.
We analysed the stories looking for examples of where people had said that 𝐵 leads to 𝐶 - for example, where someone said “I loved the regional meetings because they helped me widen my professional network”. Then all the 𝐵 s (like, Global and regional interaction) and all the 𝐶 s (like networks, friendships, support) were grouped into themes, which are the boxes in the diagram below: a Theory of Change for GYA. All the individual stories linking 𝐵 s and 𝐶 s are synthesised into one story using a pre-defined set of analysis steps.
The factors which people mentioned sort themselves into three layers, reading from left-to- right: GYA inputs, individual impacts and broader impacts. So taken together, people told us about a range of GYA inputs which led mainly to a range of impacts on individuals. These impacts on individuals also led to some broader impacts like “improved learning & teaching” on the right-hand-side. People also quite frequently mentioned direct links from GYA inputs to these broader impacts. The diagram reveals a lot of insights, like these:
People, especially women, reported that GYA activities, especially global and regional interaction like attending conferences, had many positive personal influences like establishing support networks with other young scientists.
At the heart of the theory of change is that young scientists themselves adopt GYA’s values and vision, which enables them to go on to have broader impacts like improving teaching.
Women, especially younger women, most often mentioned “solving world problems” as a broader impact, while men often mentioned interdisciplinary interaction leading to international projects and publications.
Older people mentioned GYA membership and office-holding as important inputs, and most frequently mentioned how they had learned soft skills and in particular management skills through GYA participation, whereas younger people, especially women, mentioned training inputs and how they led to career opportunities.
Download the report from the IFRC website.
This report, written by Steve Powell, used an early version of Causal Map.
After the powerful earthquake which struck Nepal in April 2015, the International Red Cross Red and Crescent Movement (“the Movement”), among others, mobilized the full range of their resources to support the relief and recovery efforts, in line with Nepal government’s overall strategy, to support the Nepal Red Cross Society (NRCS) in leading the response. NRCS, IFRC and in-country Participating National Societies (PNSs) came to an agreement that individual partners will conduct their own final evaluations of individual projects, rather than a combined final evaluation. So it was proposed to also conduct a meta-evaluation of the individual final evaluations together with all other reviews and thematic studies conducted 2015 – 2019.
This report presents the results of that meta-evaluation.
Overall top-level theory of change emerging from the documents
A central part of the report was a qualitative synthesis of the summaries of over 30 evaluative reports. The methodology is described in the section beginning on p. 22. The design was partially deductive because some main high-level causal factors like Actions and Outcomes were pre-determined, and partly inductive because many lower-level factors were identified within these high-level groups.
The procedure was to read and analyse or “code” each of the main documents in turn. The focus was on the executive summary and conclusions sections of the primary documents, as these already represent an effort by the respective authors to summarise their own documents. The full text of the documents was considered mainly in order deepen understanding of the summaries.
This qualitative procedure, known as “coding”, made use of qualitative text analysis software, Causal Map, software developed by Bath SDR and the evaluator, which is specialised for evaluations because it makes it possible to capture reports of causal connections.
This coding approach was used in order to ensure that the reports were synthesised in a systematic and transparent way.
The coding process: Important claims of causal contribution (e.g. “This WASH training element succeeded in raising hygiene awareness, but only amongst women”) in the main documents were highlighted and used to construct a theory of change or “causal map”. Each such piece of evidence was coded as a link showing how one factor (in the example, “the WASH training element”) influenced another (in the example, “raised hygiene awareness, but only amongst women”). Each link is associated with a specific verbatim quote from the evaluative reports along with the metadata e.g. report title, date, page number, etc. This metadata captures information on the context in which the quote is relevant.
The final theory of change consists of a large number of arrows or links.
Capturing information about causal contribution in this way makes it possible to ask questions about the influences on a particular factor, and in turn what it influenced, combining information from different reports.
Generic theory of change
An initial, generic theory of change used as a starting point for the qualitative analysis.
The coding process started out assuming a generic theory of change with the above, generic, form. It gained more specific detail as the analysis proceeded and more specific details emerged: the individual causal factors were organised hierarchically under the headings in the diagram as follows.
Coding the reports as outlined above identified 470 causal factors mentioned in the reports, which is quite a large number. So a hierarchy of causal factors was created by breaking up these 470 into a smaller number of common, reusable components (“WASH”, “Design” etc).
Coding a quote from one of the main documents as a link between two causal factors.
Rather than coding this statement: “Including CEA in the design for WASH activities improved all the WASH outcomes” as a link between two monolithic causal factors, it was coded as a link between two factors which are themselves made up of a number of more generic elements.
A more efficient way to code a quote from one of the main documents as a link between two causal factors, each of which is hierarchically organised.
For example, the first part of “Including CEA in the design for WASH activities improved all the WASH outcomes” is encoded as “Design>WASH>CEA” which is also part of a broader factor “Design>WASH” which is in turn part of the top-level factor “Design”. “Design>WASH>CEA” can be read “the CEA part of the WASH part of the Design process”.
This hierarchy of factors was developed iteratively during the coding process, beginning with an initial hierarchy (just “Input” “Action” and “Outcome”), which was then modified to best fit the actual contents of the documents following the usual iterative approach applied in qualitative text analysis.