Interdisciplinary causal maps

Introduction

The causal map is a tool for literature research. Just like a city map is an abstraction of an urban space in order to help people navigate it, causal maps are an abstraction of your knowledge about a topic in order to help you (or your team!) mentally navigate through the important information you've found via literature research. The way this works is that causal maps visualize the relations between concepts that are important to your research topic.

In principle, a causal map consists of two things:

A collection of components that are considered relevant to a given research question
An explication of the causal relationships between these components

In interdisciplinary settings, the causal map has an additional function beyond providing an overview of your current understanding. It can help multidisciplinary teams to integrate their work and arrive at an interdisciplinary explanation of a topic. This is because causal maps can help you explain concepts from one discipline in terms of concepts from another discipline. In this sense, they are visualizations of the process of unification through mechanistic explanation (see Bechtel, 2007; Craver & Darden, 2013 for the philosophy-heavy description of this and Tijms, 2021 for a blog post on the topic).

Building a causal map

When you talk about a topic (e.g. human reasoning), there will be some things that pique your team's interest. For the purpose of this document, let's say your team is interested in the formation of conspiratorial beliefs and decides to tackle the topic by looking at findings from psychology, neuroscience and sociology.

A search through the psychological literature reveals that conspiracy belief is associated with a wide array of psychological factors, ranging from cognitive style to social status. Some of the work is correlational, other work makes use of experimental interventions and suggests causal relations. While organizing the information, you might arrive at the following tentative understanding (which is far from complete, by the way).

Now, in the process of reading papers, you noticed that not all these links are equally well-supported by evidence. Correlations do not equal causation and in some cases individual experiments might not generalize well. Or perhaps the low-powered studies are in need of replication. This is why it's good to also make notes for the causal links you are writing down: what's the evidence and which concerns do you have? Perhaps at a later stage you will abandon initial links on your causal map, because you conclude that supporting evidence is lacking.

Integrating causal maps

Besides the psychological literature, your team has also looked at sociological findings. Just for the purpose of this document, let's assume that this literature reveals the following causal map.

Again, for each link you need empirical or theoretical support and you should keep notes about how substantiated the causal claim really is. In addition, you can look at whether this map can be recombined with the psychological one. There is certainly some correspondence between the "feelings of powerlessness" that have been identified by sociological research and the "low experienced control" that was operationalized in psychological experiments. This then warrants a closer look (and a discussion!): would it be reasonable to causally connect "feelings of powerlessness" to "low experienced control" and to drop the box "low socio-economic status"?

Similarly, neuroscientific research may provide some findings that explain the psychological traits in terms of neural substrates. Integration should always be a careful process: team members with expertise about the respective fields should agree that it is reasonable to connect or merge components in the causal map.

Keeping the map up to date

The integrated causal map should be a helpful summary of what your team understands about your shared research interest. This means finding the sweet spot between too much simplicity (skipping over important elements) and too much complexity (adding so many interconnected components you lose overview). As your understanding grows, you will find yourself adding, removing and merging components and links on the map.

This is a bit of a chore, but it is valuable. If you can see at a glance what you know and understand, you will also be able to see gaps in your knowledge. This can help you focus in the exploratory phase of your research. Similarly, you might see that some corners of the map show convergence (or conflict!) between disciplines and that means there's something interesting going on for interdisciplinary research.

In addition, keeping the map up to date will keep your team aligned during the exploration process and it will prepare you for the subsequent phase, in which you take your full understanding and consider how it could be put to use - at that point, the causal map can prompt interesting (research) questions.

Changing the explanandum

You started out with a specific explanandum, but perhaps as time progresses you care less and less about this component, while a different portion of the map draws your attention. This may because there are more interesting questions in that portion of the map or because there is more disciplinary overlap in that portion.

It is okay to shift to a new explanandum during the exploration process -- in fact, it means you are putting the mapping to good use! However, there's a best practice of only doing this in the first half of your exploratory process. In general, changing your explanandum also changes the relevance of the literature you've reviewed so far and increases the need for additional literature.

Things to be mindful of

In practice, many students overcomplicate the map, because they want to make it exhaustive. This defeats its purpose: a complicated map is no longer a useful tool to keep a clear overview of factors and mechanisms that are relevant to your project. Keep pruning, focus on specific relations you find interesting and use those as a base for your project.
Causal maps are summaries to help you keep an overview of things. They map your understanding of the topic, not the topic itself.
The causal map approach makes two philosophical assumptions you might not subscribe to.
1. It assumes an explanation-focused approach to science. There are objections to this view, most notably those from logical positivism (Godfrey-Smith, 2003), but the explanation-focused approach is useful for cross-disciplinary integration.
2. It assumes that it is in principle possible to offer mechanistic, causal explanations for the relations between phenomena and that such explanations afford unification of disparate fields (Bechtel, 2007; Craver & Darden, 2013).
There is no clear stopping point when expanding the causal map - you can spend your whole life expanding any given map, yet you probably shouldn't. Use your own judgment (and possibly that of your teacher) to see whether your understanding of the topic is deep and multidisciplinary enough for the project you are doing, or whether you still need more expansion.

References

Bechtel, W. (2007). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. Psychology Press.

Craver, C. F., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. University of Chicago Press.

Godfrey-Smith, P. (2003). Theory and reality: an introduction to the philosophy of science. University of Chicago Press.

Tijms, V. (2021, April 9). Causal maps as a tool for interdisciplinary integration. Connecting Cells Blog. https://www.connectingcells.com/causal-maps-as-a-tool-for-interdisciplinary-integration.