Evaluative Criteria

Cost-effectiveness

To be recommended, a non-profit intervention must be expected to beat a specific bar for impact. For Global Health and Development ideas, the historical bar for cost-effectiveness has been chiefly below USD 60 per consumption doubling or USD 150 per Disability Adjusted Life Year (DALY) – the more cost-effective below that bar, the better. For Animal Welfare ideas, the bar is over 30 Suffering Adjusted Days (SADs) averted per USD. Depending on the problem area, we are likely to adjust the bar, and we review said bar ahead of each research round. 

Maximal impact

We aim to maximize the expected value of the potential non-profits we can start; this involves balancing cost-effectiveness considerations with others, such as the potential scale of the intervention (e.g., the number of people a non-profit could reach to treat a certain illness). 

Well-evidenced

A non-profit intervention idea must have robust evidence to support it. We adjust what robust evidence means based on reasonable expectations for a certain field (e.g., whether one could reasonably expect to find randomized trials on a subject). 

High chance of success

We attempt to forecast the likelihood that a non-profit intervention idea would achieve its aims based on various inputs.

Strong Theory of Change (ToC): 

We favor robust ToCs with few speculative assumptions and a rational connection from inputs to impact. 

Clear additionality

We recommend non-profit ideas when we have identified a clear role and space they would fill within their ecosystem and, most importantly, would lead to counterfactual impact by not duplicating efforts. 

Ecosystem funding considerations 

We attempt to avoid situations where, by introducing new non-profits into space, other organizations that we view to be doing good work would miss out on funding. 

By relevant, we mean an idea considered a good fit with the CEIP. This includes, but is not limited to, the following factors: 

• Risk (reputational, security, risk of harm to service users, etc.): Our risk appetite varies across and within rounds depending on the portfolio and other strategic priorities. 

  • As a default, we are very risk-averse when risking harm to service users. We will likely decline to recommend non-profits with ambiguity about potential harm to service users. 

  • Our appetite for reputational harm varies and is considered more on a case-by-case basis. We are often less risk-averse when working on issues that affect non-human animals. 

  • We consider the security risk of working in volatile or unsafe environments and prefer non-profit intervention ideas offering safer work environments. Ultimately, though, where to work lies with the incubated non-profit leaders, not us. 

• Incubatee profile fit: We only cause a positive impact in the world if participants in the CEIP choose and deploy our non-profit intervention ideas. We therefore consult with other AIM departments to ensure our choices reflect both:

  • Ideas likely to be picked up by participants: We avoid recommending ideas that will not attract participants. This is more of a disqualifier than a factor that increases the chances of recommendation (i.e., an idea is unlikely because it will be attractive to incubates, all else equal). 

  • Ideas that fit the profile of our typical incubatee: Our typical incubatee is problem-agnostic and focuses on human or animal welfare. They are often generalists with no topic-specific skills. By choosing ideas suited to that profile, we look to increase the odds that the non-profit organization will have a good leader. 

• Portfolio within the round: In any given research round, we aim to diversify the types of ideas we recommend. This means that all else is equal; we prefer an idea with a different implementation model or working on different problems rather than the same. 

• Portfolio across AIM incubation: More widely, we also aim to diversify the types of bets we make across rounds, ranging back to our first incubated non-profits. Keeping in mind the difficulty of attributing the success or failure of certain non-profits to the research we do (rather than luck, co-founder quality, funding availability, and more), we try to learn from prior experience and adapt our thinking about which types of implementation models, countries of action, and other factors seem to be aligned with reaching a successful outcome.