Create evidence-based theories of change
Consultancy package
Consultancy services: more detail
At Causal Map Ltd, we don't just provide powerful AI tools - we're your partner in uncovering the rich causal stories hidden in your data. Our bespoke consulting services combine cutting-edge AI technology with deep expertise in causal mapping to deliver efficient results and insights.
How Our Consulting Process Works
Every research project is unique, which is why we always begin by having an in-depth conversation about your specific research questions and goals. We'll work closely with you to understand the nuances of your project and recommend the optimal mix of methods to meet your needs. This collaborative approach ensures that our analysis aligns with what you're looking to learn from your data.
Causal Analysis
Our consulting leverages OpenAI's advanced GPT-4.1 family of models to quickly and accurately map causal connections across large text datasets. While AI can occasionally miss nuances or make errors, our expert consultants work closely with the AI, fine-tuning it and reviewing its output to ensure reliable, insight-rich results tailored to your research needs. In addition to causal mapping, the use of AI allows the Causal Map team to conduct other qualitative analysis techniques, such as thematic coding and social networking analysis.
We offer a range of service options that can be customised based on the scope and complexity of your research.
ㅤ | What’s included | Consultancy days | Why you’d want it |
Basic | Online meetings, AI-assisted causal mapping of the dataset, basic 5-10 slides presentation of results with causal maps, tables and analysis. The causal mapping process* includes several iterations to better answer research questions and to improve how the results will be presented | 2 | - the causal mapping helps to synthesise and visualise findings from the desk review and ToC review and can provide insights to the report- uncover the (expected and unexpected) causal pathways of the program- understand how other actors influenced the project |
“Path/Thread tracing” | Trace the paths from and to factors of interest (eg from your intervention to valued outcomes), showing only stories which were mentioned in their entirety by one or more sources | 0,5 | - In order to understand the specific causal pathways connected to the program activites. - Or identify what are the direct and indirect influences of specific outcomes.- Thread tracing avoids jumping to conclusions when assembling stories from different sources. |
Sectors/Regions/Domains-based reporting | Top-level and detailed maps and tables for each sector/region/domain, showing different perspectives (like type of source, age group, gender etc) | 2 | - trace contribution pathways and strengthen the contribution analysis.- Compare causal maps across countries to identify common patterns and context-specific factors. |
Other queries of document and interview data | Beyond causal mapping, such as asking questions to the AI about your dataset. It is completely independent of causal coding. | 0,5 | Ask questions that aren’t necessarily causal to understand more about the dataset. |
Social Network Analysis | Similar to causal mapping, but showing relationships between stakeholders | 0.5 | Understand how different types of stakeholders in different countries form networks, who is central, who is peripheral. |
Ongoing support of the research team | Advice and consultation on how to collect and structure data to improve data analysis performance | 2 | - Ensure data collection aligns with analysis goals- Improve the quality and usability of collected data- Get expert guidance throughout the research process |
Multiple language reports | Get the analysis report, table and maps translated into other major languages like Spanish, German, Portuguese, French etc. | 1 | Analyse documents from different languages and print causal maps in English and any other major spoken languages needed |
Participatory Analysis Session | Online session with the research team and other interested stakeholders to present and discuss the findings. | 1 | - Understand how to read and analyse causal maps- Make sense of the expected and unexpected causal pathways uncovered in the analysis- Brainstorm how to use the findings in your report - Ask questions about the analysis |
The consultancy days are just an estimation and can be changed depending on the scope of the project.
On the table above are the individual components, but you can also see pre-made consultancy packages if you’re not sure how to combine the components.
*The causal mapping process can integrate different tailored approaches, including:
- Document review: Develop high-level causal maps to visualise programme-level causal pathways and assumptions.
- ToC comparison: Analyse and compare the explicit ToC with the implicit ToC “hidden in the data.”
- KII support: Identify potential contribution claims and alternative explanations to refine KII questions.
- Identify emerging factors: discover how your sources conceive of the key causal factors and the words they use to describe them
- Overarching synthesis: Create integrated causal maps consolidating programme-level and sector/region/domain-specific findings.
- Unexpected pathways: Uncover and communicate unexpected causal pathways or patterns.
These options can be customised based on your specific needs for the evaluation.
Causal mapping can:
- Enhance the rigour of contribution analysis by visually representing checkable causal pathways and alternative explanations.
- Facilitate participatory analysis and learning processes
- Provide a standardised way to check Theories of Change against empirical evidence
- Provide a structured approach to analysing qualitative data from diverse sources.
- Offer a powerful tool for communicating complex programme logic and evaluation findings.
- Uncover and communicate unexpected causal pathways or patterns.
- Understand the impact of the programme’s activities across different regions/countries.
It can also help answer questions like these:
- Are there unexpected outcomes?
- What different labels are used for the outcomes that interest you?
- What other factors are contributing to outcomes, and how?
- Do your stakeholders see the world the same way as you do?
- Do some groups of stakeholders see things differently?
- What is the impact on different stakeholder groups, such as Men and Women?
- Is the system changing over time, and why?
Causal mapping is rigorous, with publicly available guidelines for (manual) data collection, coding and analysis going right back to 1976 (Wrightson, 1976).
At Causal Map Ltd we believe that the use of AI should also follow published guidelines to ensure transparent and valid results. We are not comfortable with procedures which rely on giving the AI the freedom to make evaluative judgements. So we do not, for example, ask the AI “what are the main or most important causal stories in the document”. We do not ask the AI to make summaries. Instead, we use the AI only as a tireless low-level assistant to exhaustively and transparently identify large numbers of individual causal claims (ideally, every single one of them) within the texts. We follow established qualitative social science procedures for coding, with the aim that each step of the process from coding to analysis is transparent and verifiable. We don’t rely on AI to simplify the coded data or decide, for example, which themes are most important.
Costs
We believe in full transparency around costs, so you can make informed decisions about investing in causal insight for your organisation.
We anticipate that a simple causal mapping project will take a minimum of 2 days’ billable time spread out over a week or two, not including additional iterations of the research design and any additional reporting. Our standard consulting rate is £695 + VAT per day.
These rates are negotiable for particularly interesting use cases and for clients from the global South.
Email us at hello@causalmap.app to talk about the consultancy services.
