💐🌸🌹🌻🪻 Causal mapping: a Garden of Ideas#
Some brief one-page bullet-point summaries of some of our key published papers. Enjoy.
At Causal Map, we call the individual links just "links" and we call the collection of multiple "co-terminal" links, i.e. the set of links starting at X and finishing at Y, a "bundle of links" or a "links bundle" or just a "bundle". In the special case where there is only one link in the bundle, then the link = the bundle.
This chapter is a new set of working papers about causal mapping.
What is causal mapping? What are its strengths and weaknesses? How does a causal map differ from a systems diagram? This chapter has some answers.
At Causal Map we are relatively agnostic about data collection. We are most interested in causal evidence and beliefs derived from different sources.
In this chapter we present some of key general principles about how to do causal mapping which we at Causal Map Ltd (and, most of the time, at BathSDR) have adopted.
Causal coding is fascinating but can take a lot of time. Using AI to help you is pretty easy, especially if you provide a codebook of factor labels which the AI has to use, as we will explain in this chapter, when we've finished it!
In the previous chapter we introduced minimalist causal coding:
The fundamental output of causal mapping is a database of causal links. If there are not too many links, this database can be visualised "as-is" in the form of a causal map or network. But usually there are too many links for this to be very useful, so we apply filters.
Because the output is a structured network, we can apply a range of queries to explore the data. This gives us a library of pre-existing approaches to ask practical questions about the causal landscape described by the participants...
Causal mapping has been used in many different fields. In this chapter we look at how it can be applied in evaluation; its strengths and weaknesses.
Causal mapping is also a kind of Qualitative Data Analysis (QDQ). How does that even work? This chapter explains.
This guide shows how to create a simple causal map in Kumu.io using a spreadsheet. It includes:
How can we improve rigour and even reproducibility when using AI in social science? This chapter suggests some answers.
In this chapter we look at some examples of specific workflows in causal mapping, mostly illustrated with the Causal Map app. It's work in progress, we only have a couple of pages at this point.
2025-12-10 Here are some examples of work with Causal Map and causal mapping, and also with Qualia interviews.
This chapter explains how individual consultants and agencies can include Causal Map and/or Qualia in their next bid.
Here are some thoughts from a couple of years ago when genAI first hit us, plus some thoughts about where we are going with it.
Licenses, how to cite Causal Map, and a bibliography.
How to use the Causal Map app.