Doing Good Badly? - Michael Plant's thesis, Chapters 5,6 on Cause Prioritization

By EdoArad @ 2021-03-04T16:57 (+75)

In a recent answer to Issa’s Why "cause area" as the unit of analysis?, Michael Plant presents his take on cause prioritization and points to his thesis. As part of my cause prioritization analysis work with QURI I read the relevant parts of his thesis and found them interesting and novel, so I want to bring more attention to it.

In his Ph.D. thesis, Michael Plant (the founder of Happier Lives Institute) reviews the foundations of EA, presents constructive criticism on the importance of saving lives, sheds more light on how we can effectively make more people happier, describes weaknesses in the current approaches to cause prioritization and suggests a practical refinement - "Cause Mapping".  In this post, I summarize the key points of chapters 5 and 6 about cause prioritization of Michael's thesis.

Main points

1. Cause areas can be thought of as "problems", while interventions can be thought of as corresponding "solutions".
2. Cause Prioritization is an attempt to implicitly compare between the best achievable interventions in each cause area, without knowing in advance what are the most promising interventions.
3. We should be wary of dismissing cause areas too early - it's easy to miss out on the best interventions and we might be discouraged from rechecking them at a later time.
4. "Cause Mapping", a systematized method for cause prioritization, partially addresses these problems by presenting a more exhaustive approach with a greater potential to figure out what further information is needed. Broadly, it is done by mapping out potential intervention areas and the possible obstacles to applying them.

Points 1,2,3 are discussed in the next section and point 4 is discussed in the section about chapter 6. Both of these sections are my attempt to succinctly introduce Michael's work in the corresponding chapters, without my personal thoughts. I present some of my personal takeaways in the final section.

Chapter 5 - Cause Prioritization

Michael starts chapter 5 by explaining how people in EA tend to use "cause prioritization" in practice and how that can be defined -

Cause prioritisation: comparing the marginal cost-effectiveness of different problems. This is done using the three-factor framework, i.e. assessing causes by their scale, neglectedness and solvability

Cause prioritization is followed by "intervention prioritization", where quantitative cost-effectiveness estimates are used to compare interventions for promising causes. Generally, "causes" and "interventions" are just different words for "problems" and "solutions".

Cause Promisingness is Cost-Effectiveness of Best Intervention

Michael notes that the question of "How promising is a given cause area X rather than a cause area Y?" is actually "How cost-effective are the best available interventions for addressing X rather than Y?". This is natural - all we actually care about (from a consequentialist point of view) is how actions we can take translate into real-world value. This, then, raises the problem of how can we in fact separate cause prioritization from intervention prioritization

[...] it’s not immediately clear how we can determine which causes are higher priority prior to considering, at least implicitly, the interventions we might use in each case and how cost-effective those interventions are. [...] The explanation seems to be that we can evaluate problems as a whole if and when we can say something about the cost-effectiveness of all the solutions to a given problem.

Scale, Neglectedness and Solvability as Negative Indicators

One way to use the scale-neglectedness-solvability framework is to spot cases where the cause area clearly doesn't seem promising. Michael writes these explicitly -

1. Small Scale: If the problem is so tiny and it's clear that putting any effort toward solving it would be a waste.
2. Low Neglectedness: If the problem will be solved by others regardless of what we do, and our actions have no (or tiny) counterfactual impact.
3. Low Solvability: If the problem is not solvable by any means.

Overall

In total, this analysis suggests that we should conceptualize our approach to prioritization as having three steps:

1. ‘Screen out’ problems 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. 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.

Issues with this approach

Despite the reasonableness of this approach, there are two potentially important problems with this framework that Michael raises:

• First, this method requires us to be aware of existing solutions, which we might easily miss.
• Second, if we have thrown out a cause area because we overlooked something, the method discourages us to look again.

This leads Michael to suggest a more exhaustive approach.

Chapter 6 - Cause Mapping

Michael suggests a systematic method for generating additional potentially promising causes, which helps to refine our understanding of our original causes and relations between them. Broadly, this is done by listing the types of actions one can take and their potential obstacles per each problem we care about solving, and then see how these obstacles can be addressed (and by doing that we have a new cause area).

Let's break this down, using Michael's definitions and his examples from the perspective of increasing well-being (of people alive today). In the thesis work, this method had actually helped Michael discover potential avenues for further research and serves as a basis for further work in happiness research.

1. List primary causes. We start by listing the main problems which we want to solve. These are problems that we care directly about solving, rather than solving as means to solving other problems, and are thus named "primary causes". We screen out primary causes where it's clear that all of their solutions are not cost-effective by appealing to one or more of scale, neglectedness, and solvability individually (as above).
   1. Focusing on well-being, Michael lists Mundane, sub-optimal happiness; Mental health; Pain; and Poverty.
2. List main mechanisms. For each primary cause, we can list the different types of methods that can make progress on them. These basically intend to capture the possible actions we can take and the types of interventions available.
   1. In the case of happiness, Michael offers an over-arching categorization into six categories that broadly apply for all four primary causes:
      • External: altering someone’s objective circumstances, such as wealth, education, physical environment or the society they live in.
      • Temporal: how people choose to spend their time.
      • Psychological: changing how people think, e.g. cognitive treatments for
      • Chemical: using mood-enhancing substances, e.g. alcohol, painkillers or anti mental health. depressants.
      • Physical: direct manipulation of the body or brain, e.g. surgical procedures, Deep Brain Stimulation (‘DBS’).
      • Biological: genetically modifying people to be happier.
3. Assess main obstacles in the way of each mechanism. These obstacles might differ in magnitude for different people or different organizations as they have different comparative advantages and disadvantages, so prioritization should depend on the specific situation we are in.
   1. There are five main types of obstacles presented:
      1. Research: the mechanism is (theoretically) usable, but more know-how is required.
      2. Behaviour/motivation: people don’t want to use the mechanism, even though it’s available.
      3. Education: people would use it, but they don’t know about it.
      4. Resources: people know about it and would use it but can’t afford it.
      5. Policy: the state needs to act before the mechanism can be used.
4. List solutions. Each combination of a mechanism and an obstacle for it presents us with an opportunity to generate solutions, or particular actions we can take to do good.
   1. Taking a look at the primary cause of "(unhappiness caused by) poverty". The main way to directly address it is by the "external" mechanism of giving people more resources or by means that they could have bought had they had more money. Here we know quite a bit about the most promising potential interventions. Givewell's recommended charities are one good option, and their main obstacle to do more good is more money ("Resources"). Thus efforts to increase funding going there (say, by donation or fundraising) might be good. Another relevant way to cause change is around institutional change, whose main obstacle is "Policy" - leading us to suggest solutions around international aid reform, say.
5. Set out Secondary and Meta Causes. (These will be explained below)
6. Evaluate the solutions for cost-effectiveness.

Before we explain the last two steps, take a look at the following table where Michael suggests some solutions in the case of happiness interventions. Rows are the Mechanism, columns are the Primary Causes, and for each solution, its corresponding Obstacles are in parenthesis.

Taking a look at the table, we see that many solutions are applicable to several primary causes. This is reasonable, as the different problems have similar characteristics and obstacles. Therefore, our next stage is to cluster together similar solutions. These solution clusters by themselves can be considered "cause areas" (or possibly "intervention areas"). Michael suggests distinguishing between two types of solution clusters.

Secondary Causes are types of actions or solutions that by themselves partially solve the primary cause. The distinction of a "primary" from a "secondary" cause is by noting that the first is a problem and the latter is a type of intervention. Meta Causes are, in contrast, only instrumentally useful in that they only do good by having beneficial consequences on several (primary or secondary) causes instead of doing good directly.

The following diagram continues the example in the table above, where secondary causes are linked to the relevant obstacles they address (and meta causes has arrows for illustration purposes).

To prioritize meta causes one needs to have a very detailed understanding of all of the other relevant causes. Michael does not say much here.

In regards to secondary causes though, Michael writes the following.

The secondary causes are best understood as a ‘longlist’ of areas for further investigation in the sense that we hope they will contain all the good options, but not all the options they contain are good—by differentiation, we would say a ‘shortlist’ would have only good options.

making progress on evaluating the secondary causes requires doing one or more of three tasks:

• First, running new empirical experiments to test key claims. For instance, running randomised-controlled trials using happiness measures to test the cost-effectiveness of mental health and anti-poverty interventions in the developing world.
• Second, building cost-effectiveness models of interventions where evidence already exists, such as on the effect of positive psychology programmes that teach children to be happier.
• Third, establishing the particular places money could be donated to and comparing the effectiveness of particular organizations in that field.

Conclusion and Personal Thoughts

I, and others, think that there is room for a lot more useful work in cause prioritization. Taking some time to read more about this subject and how people in the community have thought about cause prioritization, there is clearly some confusion around the basic terms and processes involved, and there might be better ways to do cause prioritization which are right around the corner and better means to coordinate around our project of figuring out how to do the most good.

Overall I agree with the main thrust of this work, and I really enjoyed reading this. I especially think that it is important to be wary not to dismiss causes too soon (and give forgotten cause candidates further thought). I also think that when we think about a cause we should (mostly?) keep in mind the potential interventions in that area and map these thoroughly.

I'd be interested in comparing this approach to cause areas to something like GiveWell's definition: “a particular set of problems, or opportunities, such that the people and organizations working on them are likely to interact with each other, and such that evaluating many of these people and organizations requires knowledge of overlapping subjects.”

Another interesting point raised in this text is the process of defining secondary and meta causes. Generally speaking, a lot of causes have very complicated theories of change, and I'm curious to see a general framework that can handle interrelations between causes.

Thanks to Michael Plant for reviewing this post and giving useful feedback.