Introducing cause mapping

In the preceding chapter, I examined the priority-setting method that is typically used by effective altruists (EA) to determine which of the world’s problem we should focus on if we want to do the most good; I dubbed this ‘EA method’. The EA method seems to have two steps. The first is to evaluate causes (i.e. problems), for instance, poverty, mental health, factory farming, using the ‘three-factor cause prioritisation framework’, which involves assessing the scale, neglectedness and solvability of those problems (the details of this assessment are not important here).¹ This first step, it seems, is supposed to be prior to, and relevantly distinct from, the second step: making cost-effectiveness estimates of particular interventions (i.e. solutions) to given causes. I

1 See MacAskill (2015) at chapter 10, MacAskill (2018).

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argued that this conceptualisation of the priority-setting process isn’t quite right and the EA method is better conceived of as having these three steps:

1. ‘Screen out’ problems where it’s clear all their solutions are not cost-effective.

This is done by appealing to one or more of scale, neglectedness, and solvability individually.

2. Make an intuitive cost-effectiveness evaluation of the most promising solution(s) to each problem. This is done by combining scale, neglectedness and solvability.

3. Make explicit, quantitative cost-effectiveness evaluations of particular solutions to problems.

If we’ve done steps 2 or 3 for any two problems, we can then say which of those two problems is higher priority (in the sense of having the more cost-effective solution).

While we might say ‘problem A is a higher priority than problem B’, e.g. poverty is a higher priority than climate change, what is ultimately being compared is the cost-effectiveness of particular solution to each problem, namely the solutions that seem most cost-effective in each case.

Where does this leave us? Suppose you are trying to do the most good. You already know you want to work out the cost-effectiveness of the different things you could do with your resources. Suppose someone tells you about the EA method, which suggests you “prioritise the world’s problems by assessing them for scale, neglectedness, and tractability”. Does knowing about the EA method help you, in the sense it gives you practical guidance for how to proceed? It doesn’t seem to provide much help; when push comes to shove, it turns out “assess problems for scale, neglectedness, and tractability” is effectively equivalent to “assess problems for their cost-effectiveness”.

Further, “assess problems for their cost-effectiveness” is, in turn, effectively the same

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as the suggestion “work out the cost-effectiveness of different things you could with your resources”. You already knew you needed to do that, and it’s not clear the EA method has given you new tools to make that assessment.²

Given we’re trying to do the most good, what we want, presumably, is some method, or procedure, that helps organise our search for the most promisingly cost-effective solutions to the world’s problems. It seems the EA method does not provide much assistance.

In this chapter, I develop a novel (but quite simple) approach, cause mapping, which attempts to provide a structured approach to organising this search. I explain and motivate it in general terms in this section. In the next sections, I put this method to work where the aim is improving lives—increasing well-being whilst people are alive—

where well-being is taken to consist in happiness.

I’ll now explain how cause mapping works; this requires the specification of some terms to differentiate different parts of the process. First, we list the primary causes, the problems we want to ultimately solve. Second, we list the mechanisms, the different types of methods that make progress on the primary causes. Third, we list the obstacles, the barriers stopping those mechanisms from being used. Each combination of a mechanism with an obstacle gives us a solution—a particular action we can take to do good. Hence combining the different mechanism-obstacle pairs gives us a list of solutions. By looking at the solutions and seeing what shared obstacles they have, we can then form a list of secondary causes. By seeing what obstacles there are to the secondary causes, we can also list some meta-causes.

2 This case is perhaps analogous to someone knowing they want to determine the volume of objects and being told “ah, volume is height * depth * width”. It’s unclear if the listener has learnt something new.

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This process may seem abstract, so an example of one part of the map will help. Mental health would be a primary cause. Providing psychotherapy for mental health is a mechanism for that primary cause, i.e. it improves mental health if it is used. However, having established a mechanism for the primary cause, we can then ask what obstacle(s) is preventing that mechanism from reaching everyone who would benefit from it. Here, money is the obvious obstacle. Hence one solution (for a philanthropist) is to fund psychotherapy for mental health; the solution combines a mechanism with a way of allowing that mechanism to be used. There are a number of specific types of psychotherapy that could be provided, so we can group these together and say that generally ‘increasing access to psychotherapy’ is a secondary cause. To note the distinction between primary and secondary causes, the former refers to the type of problem we want to solve, the latter to an action (or type of action) we take to solve it.

Regarding meta-causes, we might think encouraging others to give more to charity in general is a better way of increasing access to psychotherapy than funding it ourselves.

Hence ‘encouraging altruistic behaviours’ would be a meta-cause—it is causally

‘upstream’ of actions which ultimately do good and has an impact indirectly through changing the behaviour of other agents.

Attempting to list all the possibilities at each step—all the primary causes, all the mechanisms, etc.—is clearly unrealistic, hence that is not what I suggest. Instead, I propose only to list the priority primary causes and their main mechanisms and obstacles. How are the priority primary causes determined? Through the same method used in the first step of the (reconceptualised) EA method above: we rule out unpromising primary causes using our intuitive judgements. Specifically, we see if any of the solutions to the primary causes seem more effective than the most cost-effective solutions we are already aware of. The aim is to do the most good and our

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best existing option(s) ‘set the bar’: the aim is to clear it with something even better.

Hence, there’s no point listing the mechanisms and obstacles that apply to unpromising primary causes. A similar process is applied thereafter: I won’t list all the mechanisms and obstacles to the priority primary causes, only those that seem they could lead to the most cost-effective solutions.

Broken into its constituent steps, the cause mapping process functions as follows:

0. Divide the world up into primary causes

1. ‘Screen out’ primary causes where it’s clear all their solutions are cost-ineffective. This is done by appealing to one or more of scale, neglectedness, and solvability individually.

2. Of the primary causes that remain, list the main mechanisms available for each primary cause.

3. Assess the main obstacles in the way of each mechanism.

4. Create a list of solutions by combining 2. and 3.

5. From the list of solutions, set out secondary and meta-causes.

6. Evaluate the solutions for cost-effectiveness.

Readers may wonder how this is different from the EA method, either as originally stated or on my reconceptualisation. There are two comments to make.

First, I do not think it’s the case the above steps are incompatible with, or different, from those in the EA method. Rather, I am simply making explicit the different steps that were already implicit in EA method (and that one must undertake when thinking about how to do the most good). What occurs in steps 2-4 of cause mapping is something that must implicitly occur between steps 1 and 2 of the reconceptualised EA method—to get to step 2 of the latter, we must acquire a set of particular solutions from somewhere. Cause mapping is about filling out the items on that set.

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Second, the EA method, at least as articulated by MacAskill and others, does not encourage or require us to map out the different mechanisms, obstacles, solutions and so on.³ While this is not a radical innovation, it does seem useful to carefully work through the different steps in the hope of discovering altruistic opportunities that were not prima facie obvious—this is, indeed, what I found when applying it to the question that follows. Given there seems to be no a priori means of working out what our altruistic priorities are in the actual world, this sort of approach seems to be the best we can do.

Thus, cause mapping can be seen as an attempt to break down the EA method into its smallest distinct steps, record the most promising items considered, and identify how those items connect together to produce a reasonably comprehensive list of altruistic options. Once that list is in hand, the subsequent step is to evaluate those options for cost-effectiveness.