Back in April 2018, I spent some time trying to understand the hierarchy/structure/classification of cause areas. I did this at the suggestion of Vipul Naik, who wanted to (1) categorize cause areas treated on the Cause Prioritization Wiki so that there was more structure to it than that of a jumble of 100+ cause areas, and (2) make the analysis of cause areas more systematic. (I believe he was also interested in this because the Donations List Website that he created also needed a better ontology of cause areas.)

Some of the outputs of that investigation are:

  • A list of existing classifications of philanthropy
  • A directed acyclic graph of existing cause areas (where means " has as a sub-cause" or "if I am claiming that I work on , then I can also claim that I am working on ")
  • A list of potential properties with which to classify existing cause areas
  • A table of "form of altruism" vs "beneficiary group" ("form of altruism" and "beneficiary group" are two of the "potential properties" in the previous list, so this table crosses these two properties, resulting in a two-dimensional grid)
  • A generic linkdump and rambling on taxonomies

I came away from the above investigation feeling pretty confused about the nature of cause areas. Given just a description of reality, it didn't seem obvious to me to carve things out into "cause areas" and to take "cause area" as the basic unit of analysis/prioritization (which is what cause prioritization is all about).

Some thoughts/intuitions that contribute to this feeling are:

  • As explained (EA Forum link; HT Edo Arad) by Owen Cotton-Barratt back in 2014, there are at least two meanings of "cause area". My impression is that since then, effective altruists have not really distinguished between these different meanings, which suggests to me that some combination of the following things are happening: (1) the distinction isn't too important in practice; (2) people are using "cause area" as a shorthand for something like "the established cause areas in effective altruism, plus some extra hard-to-specify stuff"; (3) people are confused about what a "cause area" even is, but lack the metacognitive abilities to notice this.
  • A cause area can try to "seem" big or small by lumping together more and more things in the world (or alternatively excluding more things from itself). Do we compare "animal welfare improvement" against "agent foundations research", or against "technical AI safety work", or against "technical, strategy, or policy work in AI safety", or against "existential risk reduction", or against "applied mathematics related to futuristic technology"?
  • More generally, if we take some basic unit of action like "1 person-year of work" then we can form sets of actions and call those sets "cause areas" (these sets don't necessarily form a partition, i.e. there might be actions contained in multiple causes and actions not contained in any cause). But then we can imagine defining some arbitrary "cause area" that just picks out the most high-value actions and declares it "the most important cause". Of course, finding which actions are contained in this "most important cause" would be difficult, and the task of cause prioritization would seem to be reduced to this search process.
  • I can imagine an argument taking place where the opponent of a cause area picks some ineffective actions within the cause area while a supporter picks effective actions, so they disagree regrading the overall effectiveness of the cause area despite agreeing about the effectiveness of specific actions. Maybe even a motte-and-bailey argument where the supporter draws a tighter boundary around the cause when attacked, and loosens the boundary at other times to be able to call their preferred interventions effective. (I don't actually know if such arguments are taking place, so this is just a theoretical concern at the moment.)
  • One way looking at cause areas might be useful is from an evaluator's perspective of "what skills/domain expertise do I need to be able to evaluate specific programs/research topics?" If skillsets tend to "unlock" a bunch of potential programs at once, then there might be a natural-seeming boundary around these programs, which might correspond to our intuitive notion of cause area. But this seems to depend on the order in which various skills are acquired. To take an extreme case, if someone had a lot of domain-specific expertise in many domains but lacked some general skill (like generalist research skills, knowledge of statistics, programming experience) then by learning the general skill they suddenly "unlock" a whole bunch of "cause areas" at once.
  • I think reductionism and "dissolving the question" type moves have been useful in many situations, and I have a vague intuition that the notion of cause area can be reduced in some way.
  • In practice, Open Philanthropy Project (which is apparently doing cause prioritization) has fixed a list of cause areas, and is prioritizing among much more specific opportunities within those cause areas. (I'm actually less sure about this as of 2021, since Open Phil seems to have made at least one recent hire specifically for cause prioritization.)
  • I've noticed that as I learn more about a cause area, I get more opinionated about activities within it. A naive analysis cannot distinguish effectiveness within a cause area, and instead puts a uniform score over the whole cause area, whereas a more sophisticated analysis puts precise scores over each action within a cause area. So it feels like "cause prioritization" is just a first step, and by the end it might not even matter what cause areas are. It seems like what actually matters is producing a list of individual tasks ranked by how effective they are.
  • In this 80,000 Hours podcast episode Toby Ord talks about the idea of risk factors, as distinguished from risks. This seems to further complicate the situation.
  • Some recent Katja Grace posts that are relevant and that make me even more confused: Are the consequences of groups usually highly contingent on their details? and Infinite possibilities.

Why does any of this matter? Here are a couple of reasons that come to mind:

  • Practically, projects like Cause Prioritization Wiki, Donations List Website, and other efforts to categorize cause areas require some organization system that makes sense.
  • From a more philosophical or emotional perspective, I feel dissatisfied with my current understanding.
  • In terms of public discourse, people are actually using the concept of "cause area" to do further thinking. If the idea of a cause area is not a reliable one, then all of this further thinking is done on a shaky foundation, which seems worrying. I feel like these two comments by Buck Shlegeris and this post by Katja Grace are possibly doing this thing, or giving less careful thinkers the idea that this is a sound move.

I am curious to hear people's thoughts on this. I would also appreciate pointers to existing discussions (I feel like I've been paying attention, but it seems plausible to me that I've missed some).

Thanks to Vipul Naik for funding part of my work on this post, and for funding my work on cause areas that led to this post. Thanks also to Edo Arad for pushing me to finish this post.

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4 Answers

FWIW, I think it helps to think  of effective altruism along the following lines. This is more or less taken from chapters 5 and 6 of my PhD thesis which got stuck into all this in tedious (and, in the end, rather futile) depth. 

Who? As in, who are the beneficiary groups?

Options: people (in the near-term), animals (in the near-term), future sentient life

What? As in, what are the problems?

This gives you your cause areas, i.e. the problems you want to solve that directly benefit a particular group, e.g. poverty, factory farming, X-risks. 

Effective altruism is a practical project, ultimately concerned about what the best actions are. To solve a problem requires thinking, at least implicitly, about particular solutions to those problems, so I think it's basically a nonsense to try to compare "cause areas" without reference to specific things you can do, aka solutions. Hence, when we say we're comparing "cause areas" what we are really doing is assessing the best solution in each cause area "bucket" and evaluating their cost-effectiveness. The most important cause = the one with the very most cost-effective intervention.

How? As if, how can the problems be best solved?

Here, I think it helps to distinguish between interventions and barriers. Interventions are the thing you do that ultimately solve the problem, e.g, cash transfers and bednets for helping those in poverty. You can then ask what are the barriers, i.e. the things that stop those interventions from being delivered. Is it because people don't know about it? Do they want them but can't afford them, etc? A solution removes a particular barrier to a particular intervention, e.g. just provides a bednet.

What's confusing is where to fit in things like "improving rationality of decision-makers" and "growing the EA movement", which people sometimes call causes. I think of these as 'meta-causes' because they indirect and diffusely work to remove the barrier to many of the 'primary causes', e.g. poverty. 

It's not clear we need answers to the 'why?', 'when?', and 'where?' queries. Like I say, if you want to waste an hour or two, I slog through these issues in my thesis. 

I like this answer.

I think it's basically a nonsense to try to compare "cause areas" without reference to specific things you can do, aka solutions. Hence, when we say we're comparing "cause areas" what we are really doing is assessing the best solution in each cause area "bucket" and evaluating their cost-effectiveness. The most important cause = the one with the very most cost-effective intervention.

Maybe a minor point, but I don't think this is quite right, because: 

  • I don't think we know what the best solution in each "bucket" is
  • I don't think we ha
... (read more)

A lot depends on what constitutes a cause area and what counts as analysis. My own rough and tentative view is that at some level of generality (which could plausibly be called "cause area"), we can use heuristics to compare broad categories of interventions. But in terms of actual rigorous analysis, cause area is certainly not the right unit, and, furthermore, as a matter of empirical fact, there aren't really any research organizations (including Rethink Priorities, where I work) that take cause area to be the appropriate unit of analysis.

Very curious to hear the thoughts of others, as I think this is a super important question!

I agree with your first two sentences. I feel unsure precisely what you mean by the sentence after that.

E.g., are you saying that no research organisations are spending resources trying to help people prioritise between different broad cause areas (e.g., longtermism vs animal welfare vs global health & development)? Or just that there's no research org solely/primarily focused on that?

My impression is that: 

  • There were multiple orgs that were primarily focused on between-cause prioritisation research in the past
  • But most/all have now decided on one or more cause areas as their current main focus(es) for now, and so now spend more of their effort on within-cause-area work
  • But many still do substantial amounts of work that's focused on or very relevant to between-cause prioritisation, and may do more of that again later. E.g.:
    • Open Phil do worldview investigations
    • 80,000 Hours continue to put some hours (e.g.) into non-longtermist issues even if primarily longtermist issues are definitely their main focus
    • GPI are currently focused mostly on global priorities research that's relevant to longtermism. But much of that is directly about how much to prioritise longtermism in the first
... (read more)

My main thoughts on this:

  • I share the view that EAs often seem unclear about precisely what they mean by "cause area", and that it seems like there are multiple somewhat different meanings floating around
    • This also therefore makes "cause prioritisation" a somewhat murky term as well
  • I think it would probably be valuable for some EAs to spend a bit more time thinking about and/or explaining what they mean by "cause area"
  • I personally think about cause areas mostly in terms of a few broad cause areas which describe what class of beneficiaries one is aiming to help
    • If future beings: Longtermism
    • If nonhuman animals (especially those in the near-term): Animal welfare
    • If people in developing countries: Global health & development
    • We can then subdivide those cause areas into narrower cause areas (e.g. human-centric longtermism vs animal-inclusive longtermism; farm animal welfare vs wild animal welfare)
    • This is somewhat similar to Owen Cotton-Barratt's "A goal, something we might devote resources towards optimising"
      • But I think "a goal" makes it much less clear how granular we're being (e.g., that could mean there's a whole cause area just for "get more academics to think about AI safety"), compared to "class of beneficiaries"
    • Caveats:
      • There are also possibilities other than those 3
        • e.g., near-term humans in the developed world
      • And there are also things I might normally "cause areas" that aren't sufficiently distinguished just by the class of beneficiaries one aims to help
        • e.g., longevity/anti-ageing
      • I don't mean to imply that broad cause areas are just a matter of a person's views on moral patienthood; that's not the only factor influencing which class of beneficiaries one focuses on helping
        • E.g., two people might agree that it's probably good to help both future humans and chickens, but disagree about empirical questions like the current level of x-risk, or about methodological/epistemological questions like how much weight to place on chains of reasonings (e.g., the astronomical waste argument) vs empirical evidence
  • I'm very confident that it's useful to have the concept of "cause areas", to sometimes carve up the space of all possible altruistic goals into at least the above 3 cause areas, and to sometimes have the standard sorts of cause prioritisation research and discussion
  • I think the above-mentioned concept of "cause areas" should obviously not be the only unit of analysis
    • E.g., I think most EAs should spend most of their lifetime altruistic efforts prioritising and acting within broad cause areas like longtermism or animal welfare
      • E.g., deciding whether to work on reducing risks of extinction, reducing other existential risks, or improving the longterm future in other ways
        • And also much narrower decisions, like precisely how best to craft and implement some specific nuclear security policy

I'll add some further thoughts as replies to this answer. 

[I think the following comment sounds like I'm disagreeing with you, but I'm not sure whether/how much we really have different views, as opposed to just framing and emphasising things differently.]

So it feels like "cause prioritization" is just a first step, and by the end it might not even matter what cause areas are. It seems like what actually matters is producing a list of individual tasks ranked by how effective they are.

I agree that cause prioritization is just a first step. But it seems to me like a really useful first step. 

It seems to me lik... (read more)

2EdoArad5moThis seems to be true if it is possible to gradually grow within a cause area, or if different tasks within a promising cause area are generally good. This might lead to a good working definition of cause areas
4MichaelA5moI'm not sure I understand. I don't think what I said above requires that it be the case that "[most or all] different tasks within a promising cause area are generally good" (it sounds like you were implying "most or all"?). I think it just requires that the mean prioritisation-worthiness of tasks in some cause, or the prioritisation-worthiness of the identifiable positive outliers among tasks in some cause, are substantially better than the equivalent things for another cause area. I think that phrasing is somewhat tortured, sorry. What I'm picturing in my head is bell curves that overlap, but one of which has a hump notably further to the right, or one of which has a tail that extends further. (Though I'm not claiming bell curves are actually the appropriate distribution; that's more like a metaphor.) E.g., I think that one will do more good if one narrows one's search to "longtermist interventions" rather than "either longtermist or present-day developed-world human interventions". And I more tentatively believe the same when it comes to longtermist vs global health & dev. But I think it's likely that some interventions one could come up with for longtermist purposes would be actively harmful, and that others would be worse than some unusually good present-day-developed-world human interventions.
2EdoArad5moYea, sorry for trying to rush it and not being clear. The main point I took from what you said in the comment I replied to was something like "Early on in one's career, it is really useful to identify a cause area to work in and over time to filter the best tasks within that cause area". I think that it might be useful to understand better when that statement is true, and I gave two examples where it seems correct. I think that there are two important cases where that is true: 1. If the cause area is one where generally working toward it will improve understanding of the whole cause area and improve one's ability to identify and shift direction to the most promising tasks later on. 1. For example, Animal Welfare might arguably not be such a cause because it is composed of at least three different clusters which might not intersect much in their related expertise and reasons for prioritization (alternative proteins, animal advocacy and wild animal welfare). However, these clusters might score well on that factor as sub-cause areas. 2. If it is generally easy to find promising tasks within that cause area. 1. Here I mostly agree with the overlapping bell curves picture, but want to explicitly point out that we are talking about task-prioritization done by novices.
2EdoArad5moA contrasting approach is to choose the next steps in a career based on opportunities rather than causes, as Shay wrote [https://forum.effectivealtruism.org/posts/LHZBcqyCkYqmZLzij/my-career-decision-making-process#Opportunities_and_Cause_Areas] :
2MichaelA5mo(That link seems to lead back to this question post itself - I'm guessing you meant to link to this other post [https://forum.effectivealtruism.org/posts/LHZBcqyCkYqmZLzij/my-career-decision-making-process] ?)
2EdoArad5mo(thanks! fixed)

As explained (EA Forum link; HT Edo Arad) by Owen Cotton-Barratt back in 2014, there are at least two meanings of "cause area". My impression is that since then, effective altruists have not really distinguished between these different meanings, which suggests to me that some combination of the following things are happening: (1) the distinction isn't too important in practice; (2) people are using "cause area" as a shorthand for something like "the established cause areas in effective altruism, plus some extra hard-to-specify stuff"; (3) people

... (read more)
2EdoArad5moI really agree with this kind of distinction. It seems to me that there are several different kinds of properties by which to cluster interventions, including: 1. Type of work done (say, Political Advocacy) 2. Instrumental subgoals (say, Agriculture R&D (which could include supporting work, not just research)). (I'm not sure if it's reasonable to separate these from cause areas as goals) 3. Epistemic beliefs (say, interventions supported by RCTs for GH&D) (It seems harder than I thought to think about different ways to cluster. Absent of contrary arguments, I might purpose defining intervention areas as the type of work done)

In practice, Open Philanthropy Project (which is apparently doing cause prioritization) has fixed a list of cause areas, and is prioritizing among much more specific opportunities within those cause areas. (I'm actually less sure about this as of 2021, since Open Phil seems to have made at least one recent hire specifically for cause prioritization.)

Open Phil definitely does have a list of cause areas, and definitely does spend a lot of their effort prioritising among much more specific opportunities within those cause areas.

But I think they also spen... (read more)

I just stumbled upon this definition of a "cause" from GiveWell in 2013:

we’ve since moved to the cause as our fundamental unit of analysis. We’d roughly define a “cause” as “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.”

That definition seems useful to me, though of course many other definitions are possible too.

Where I found that was a link from an 80,000 Hours post from 2013 on Why pick a cause?, in which they discuss 4 key reasons:

  1. Picking a cause is one of the best things you can do to increase your impact.
  2. We think picking a cause provides you with a useful level of direction in planning your next steps, which is neither too narrow nor too broad.
  3. Picking a cause seems to be a useful way to narrow down careers based on personal factors and deeply held value judgements.
  4. Having a cause can be motivating.

So that post seems relevant here.

(I think this largely repeats the sort of points made in other answers/comments, but I felt I might as well share these links and quotes anyway.)

That one is linked from Owen's post.

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So for me, the motivation for categorizing altruistic projects into buckets (e.g., classifications of philanthropy) is to notice the opportunities, the gaps, the conceptual holes, the missing buckets. Some examples:

  • If you divide undertakings according to their beneficiaries and you have a good enough list of beneficiaries, you can notice which beneficiaries nobody is trying to help. For example, you might study invertebrate welfare, wild animal welfare, or something more exotic, such as suffering in fundamental physics.
  • If you have a list of tools, you can notice which tools aren't being applied to which problems, or you can explicitly consider which tool-problem pairings are most promising. For example, ruthlessness isn't often combined with altruism.
  • If you have a list of geographic locations, you can notice which ones seem more or less promising.
  • If you classify projects according to their level of specificity, you can notice that there aren't many people doing high level strategic work, or, conversely, that there are too many strategists and that there aren't many people making progress on the specifics.

More generally, if you have an organizing principle, you can optimize across that organizing principle. So here in order to be useful, a division of cause areas by some principle doesn't have to be exhaustive, or even good in absolute terms, it just has to allow you to notice an axis of optimization. In practice, I'd also tend to think that having several incomplete categorization schemes among many axis is more useful than having one very complete categorization scheme among one axis.