Why effective altruism used to be like evidence-based medicine. But isn’t anymore

Thanks for this very interesting and clearly articulated post. A comment specifically on the "camps" thing.

Within the people actually working on existential risk/far future, my impression is that this 'competition' mindset doesn't exist to nearly the same extent (I imagine the same is true in the 'evidence' causes, to borrow your framing). And so it's a little alarming at least to me, to see competitive camps drawing up in the broader EA community, and to hear (for example) reports of people who value xrisk research 'dismissing' global poverty work.

Toby Ord, for example, is heavily involved in both global poverty/disease and far future work with FHI. In my own case, I spread my bets by working on existential risk but my donations (other than unclaimed expenses) go to AMF and SCI. This is because I have a lot of uncertainty on the matter, and frankly I think it's unrealistic not to have a lot of uncertainty on it. I think this line (" There should definitely be people in the world who think about existential risk and there should definitely be people in the world providing evidence on the effectiveness of charitable interventions.") more accurately sums up the views of most researchers I know working on existential risk.

I realise that this might be seen as going against the EA 'ethos' to a certain extent - a lot of the aim is to be able to rank things clearly and objectively, and choose the best causes. But this gets very difficult when you start to include the speculative causes. It's the nature of existential risk research to be wrong a lot of time - a lot of work surrounds high impact, low probability risks that may not come to pass, many of the interventions may not have effect until much further in the future, it is hard to predict whether it's our work which makes the crucial difference, etc - all of this makes it difficult to measure.

I'm happy to say existential risk (and global catastrophic risk) are important areas of work. I think there are strong, evidence-based arguments that it has been under-served and underfunded globally to date, for reasons well-articulated elsewhere. I think there are also strong arguments that e.g. global poverty is under-served and underfunded for a set of reasons. I'm happy to say I consider these both to be great causes, with strong reasons to fund them. But reducing down "donate to AMF vs donate to CSER" into e.g. lives saved in the present versus speculative lives saved in the future involves so much gross simplification and assumptions that could be wrong by so many orders of magnitude that I'm not comfortable doing it. Add to this moral uncertainty over value of present lives versus value of speculative future lives, value of animal lives, and so on, and it gets even more difficult.

I don't know how to resolve this fully within the EA framing. My personal 'dodge' has been to prioritise raising funds from non-EA sources for FHI and CSER (>95%, if one excludes Musk, >80% if one includes him). I would be a hypocrite to recommend to someone to stop funding AMF in favour of CSER, given that I'm not doing that myself. But I do appreciate that an EA still has to decide what to do with her funds between xrisk, global poverty, animal altruism, and other causes. I think we will learn from continuing excellent work by 'meta' groups like GiveWell/OPP and others. But to a certain extent, I think we will have to recognise, and respect, that at some point there are moral and empirical uncertainties that are hard to reduce away.

Perhaps for now the best we can say is "There are a number of very good causes that are globally under-served. There are significant uncertainties that make it difficult to 'rank' between them, and it will partly depend on a person's moral beliefs and appetite for 'long shots' vs 'safe bets', as well as near-term opportunities for making a clear difference in a particular area. But we can agree that there are solid reasons to support this set of causes over others".

Besides evidence-based medicine, there is of course also evidence-based policy which is explicitly modelled on evidence-based medicine. They also focus heavily on RCTs and other kinds of rigorous evidence.

I founded a network for evidence-based policy earlier this year in Sweden. Here's a post where I argue that evidence-based policy is basically EA minus it's moral views.

Interesting suggestion. I don't think anyone's advocating for using reasoning without evidence (called 'a priori reasoning'), nor does anyone think that we should truly only reimplement interventions performed in particular studies without extrapolating at all. People like the future of humanity institute, in particular, are seeking to generalise from evidence in a principled way. So the question is really 'of ways to use reason to generalise from existing evidence, which is best?' It seems counterproductive to try to divide people who are all fundamentally trying to answer this same question into different camps.

Bayesian rationality is one compelling answer to the 'how do we apply reason to evidence?' that has some advantages:

• it allows quantification of beliefs,
• it allows quantification of strength of evidence,
• it's unexploitable in betting

I think your distinction rests on an overly simplistic description of 'evidence based medicine', and that to divide effective altruism into camps is likewise a false dichotomy.

(TLDR: EBM doesn't equal total reliance on meta-analyses. Evidence based medicine still requires reason. EBM without reason is just as dangerous as reason without evidence)

As most people here known, in EBM, the highest standard of evidence is a meta-anaysis of well conducted randomised, controlled, double blind trials. Unfortunately decisions that can be supported by evidence of such quality are relatively few. There are many reasons for this: trials are difficult to design and run in such a way they actually answer the question correctly; when designed well they are expensive to run; not all trial data are reported; there is a long lag time between identifying a question, conceiving and conducting the trials, analysing and reporting the data, and considering how this changes the weight of available evidence.

In general trials are most often run for investigations or interventions that can make somebody money or where regulation demands it for something novel. Drugs and devices need trials to be licensed, but once licensed they can be sold and used 'off license' for other conditions without further RCTs. It is considered unethical to withdraw what is currently considered standard best practice and replace it with a placebo in a trial. For these and other reasons there remains a huge amount of medical decision making is not supported by highest quality evidence.

However we still need to act. We can't put off our patients and ask them to come back with their perforated bowel once somebody has done a controlled trial of operating on a burst colon vs placebo. In the absence of highest quality evidence medical professionals still practice in a way that is considering and respectful of evidence. We consider lesser forms of evidence, we weigh the likelihoods based on biological theories, and we update our beliefs as new evidence comes to light. It all sounds a bit Bayesian, doesn't it?

In fact, the physicians I know who are most committed to guiding their practice with evidence and reason are prepared to act against the results of randomised clinical trials. As a recent example: a US based RCT showed that EGDT - targeting treatment of septic (ie very sick from infection) at particular numbers - was superior to usual care (ie being guided by the treating doctor). In fact they demonstrated a massive 16% absolute decrease in 30 day mortality. UK, European and US centres set about trying to replicate it, but given what was at stake this required huge and co-ordinated efforts. It took over a decade and all 3 multicentre studies showed... absolutely no benefit.
http://blogs.nejm.org/now/index.php/the-final-nail-in-early-goal-directed-therapys-coffin/2015/03/24/

In the meantime, what should the evidence-based practitioner have done? A shallow answer would be to do what the evidence said: immediately change practice to EGDT, and only update when a further RCT countered the result. But that would have been a costly mistake that subjected patients to unnecessary invasive monitoring. The results of the first trial were counterintuitive to many experts, especially those who have seen fashions for 'treating the numbers' (rather than the patient) arise and be discredited over many years. Most senior ED/ITU doctors did not follow EGDT in the decade between results because the trial data was not enough to cause them to update their practice.

It later came out that in the original trial there were several systematic ways that the intervention group differed from the usual care group, but this was not fully captured in the study report. Further, the mortality in the first trial was much higher than we see in the NHS. The overall 'story' is probably that treatment by numbers is not better than getting the basics right, which we overall do.

Reason and evidence aren't separate camps. They are both fundamentally important when you act in the real world.

DOI - studying evidence based medicine on and off since 1999.

Thanks for the post! I place myself in the evidence camp, though I wouldn't say it's a matter of "ambiguity aversion", and saying its "not [about] maximising expected utility" risks being misunderstood. As you said, there are explicit consequentialists in both camps. (Non-consequentialists can be found in both too.)

Both camps are diverse so I wouldn't want to try giving a complete characterisation of either, and someone in the rationalist camp would likely do a better job of describing their group's most common views. My personal impression is that the evidence/empiricism camp is typically not so much against maximising expected utility, but is sceptical of certain sorts of explicit expected value calculations. For example, a majority of those found on the back of envelopes, and an even larger number of those found on the back of napkins. Here are some relevant posts:

Why We Can’t Take Expected Value Estimates Literally (Even When They’re Unbiased) - Holden Karnofsky

Sequence Thinking vs. Cluster Thinking - Holden Karnofsky

Quantification as a Lamppost in the Dark - Adam Casey (h/t Sasha Cooper here)

Why I'm Skeptical About Unproven Causes (And You Should Be Too) - Peter Hurford

Thanks both for thoughtful replies and links.

I agree that it may be counterproductive to divide people who are answering the same questions into different camps and, on re-reading, that is how my post may come across. My more limited intention was to provide a (crude) framework through which we might be able to understand the disagreement.

I guess I had always interpreted (perhaps falsely) EA as making a stronger claim than 'we should be more reasonable when deciding how to do good'. In particular I feel that there used to be more of a focus on 'hard' rather than 'soft' evidence. This helps explain why EA used to advocate charitable giving over advocacy work / systemic change, for which hard evidence is necessarily more limited. It seems EA is now a broader church and this is probably for the better but in departing from a preference for hard evidence/RCTs it has lost its claim to being like evidence-based medicine.

The strength of this evolution is that EA seems to have absorbed thoughtful critiques such as that of Acemoglu http://bostonreview.net/forum/logic-effective-altruism/daron-acemoglu-response-effective-altruism although I imagine it must have been quite annoying to be told that "if X offers some prospect of doing good, then EAs will do it" when we weren't at the time. Perhaps EA is growing so broad that the only real opponents they have left are the anti-rationalists like John Gray (although the more opponents he has the better)

Where is the evidence for this claim? This all seems like reason and words :P

I think most philosophers would say that evidence and reason are different because even if practical rationality requires that you maximize expected utility in one way or another---just as theoretical rationality requires that you conditionalize on your evidence---neither thing tells you that MORE evidence is better. You can be a perfectly rational, perfectly ignorant agent. That more evidence is better than less is a separate kind of epistemological principle than the one that tells you to conditionalize on whatever you've managed to get.(1)

Another way to put it: more evidence is better from a first-person point of view: if you can get more evidence before you decide to act, you should do it! But from the third person point of view, you shouldn't criticize people who maximize expected utility on the basis of bad or scarce evidence.

Here's a quote from James Joyce (a causal decision theorist):

"CDT [causal decision theory] is committed to two principles that jointly entail that initial opinions should fix actions most of the time, but not [always]...

CURRENT EVALUATION. If Prob_t(.) characterizes your beliefs at t, then at t you should evaluate each act by its causal expected utility using Prob_t(.).

FULL INFORMATION. You should act on your time-t utility assessment only if those assessments are based on beliefs that incorporate all the evidence that is both freely available to you at t and relevant to the question about what your acts are likely to cause." (Joyce, "Regret and Instability in Causal Decision Theory," 126-127)

...only the first principle is determined by the utility-maximizing equation that's at the mathematical core of causal decision theory. Anyway, that's my nerdy philosophical lit contribution to the issue ;).

(1) In an extreme case, suppose you have NO evidence---you are in the "a priori position" mentioned by RyanCarey. Then reason is like an empty stomach, with no evidence to digest. But still it would contribute the tautologies of pure logic---those are propositions that are true no matter what you conditionalize on, indeed whether you conditionalize on anything at all.

This is a real important distinction I had never thought of.

The way I would describe this or a similar difference in personal preference is along the lines of repeatability:

1. Some people feel drawn toward maximizing expected value by addressing hypothetical events that are so bad that we wouldn’t be here anymore to think about them if they happened repeatedly or had happened at all, while
2. other people feel drawn toward maximizing expected value by addressing events that are far less bad but happen frequently.

These repeating events have the advantage that you can test the effectiveness of your intervention on them (say, with RCTs) and improve it incrementally. Hence there is a lot more solid evidence to draw on. But solely based on that consideration, there is no better preference, just as social scientist like to say that physics is so much farther along just because it’s so much easier to study.