Show Notes
GiveWell works to continually improve our research so we can make better grant decisions and direct donations where we believe they can do the most good. GiveWell’s Cross-Cutting research subteam is an important part of that effort—it investigates the impact of the programs we fund, works to resolve our largest uncertainties, and aims to improve our research. As GiveWell’s grantmaking grows, the Cross-Cutting subteam is strengthening our data collection and analysis to support that growth. In this episode, GiveWell co-founder and CEO Elie Hassenfeld speaks with Program Director Alex Cohen, who leads the Cross-Cutting subteam, about how better data can improve our decision-making, our work to gather stronger evidence, and the ways we’ve tested AI as a tool to increase our research capacity and quality.
Elie and Alex discuss: Triangulating multiple data sources to check impact: Directing donations to programs we believe will do the most good requires understanding the impact of the programs we support. Organizations provide us with monitoring data, but we know there are many ways that, despite the best intentions, this data can be biased or incomplete. To address this, we are investing in additional sources and types of data to improve the accuracy of our estimates. For example, in Burkina Faso we’re funding dried-blood tests to better understand whether caregivers are completing the second and third at-home doses of seasonal malaria chemoprevention (SMC), which provides preventive malaria medication. We think this will improve our understanding of how accurate self-reported data is and whether funding to boost coverage of second and third doses of the medication could cost-effectively help people. Setting quality standards for survey data: GiveWell often collects survey data at the end of a program to better understand its impact and answer our open questions, but survey data is prone to error. For example, GiveWell researchers shadowed surveyors in Uganda and Nigeria who worked long days under time pressure, creating conditions where hard-to-access households were more likely to be skipped. Because those households may also be the ones programs are most likely to miss, this could lead to overestimating coverage. We’re working to reduce these errors by investing in more independent data checks, and we’ve developed survey guidelines that include practices like GPS tracking and verifying whether a random sample of households was surveyed. Using AI to critique our work: GiveWell is experimenting with AI tools to increase our research capacity and improve its quality. Our cost-effectiveness models are an important part of our funding decisions, and we carefully vet them to reduce the chance of mistakes. We’ve developed and trained an AI model that, with human oversight, can catch errors that previously required tedious staff review, opening up some capacity for those reviewers to support the team in other ways. We're also experimenting with using AI to critique and identify weaknesses in our research, though we’ve had more limited success with this strategy.
By investing in more and better data and experimenting with new tools, we’re working to expand our research capacity while strengthening the rigor of our work as our grantmaking scales. Growing our research team is central to these goals, and GiveWell is actively hiring researchers and program officers. New members of our research team typically start on the Cross-Cutting subteam as part of their onboarding and training. If someone you know might be a good fit, please share our open roles! Visit our All Grants Fund page to learn more about how you can support this work, and listen or subscribe to our podcast for our latest updates. This episode was recorded on August 21, 2026, and represents our best understanding at that time.
More from GiveWell Conversations
In an effort to direct substantially more funding to the most cost-effective opportunities we can find to help people, GiveWell’s research is expanding in two directions at once: going broader, to cover more areas of global health and development, and going deeper, to improve the inputs that inform our decision-making.
GiveWell, with co-funding from Coefficient Giving, recently made our largest grant to date: $276 million to the Against Malaria Foundation (AMF) to support the procurement and distribution of 92 million nets in the Democratic Republic of the Congo (DRC) from 2027 through 2029. We estimate that the nets, 46 million of which we think would not otherwise be distributed, will avert roughly 67,000 deaths. We have supported AMF’s work for more than 15 years, and the insecticide-treated nets it procures and distributes remain one of the most cost-effective ways we've found to save lives.
When GiveWell investigates a new cause area, we’re often focused on two priorities: directing funding where we think it can help people most and generating evidence to inform larger investments later. Family planning is a clear example. Recent foreign aid cuts—including reductions from the US, UK, and Dutch governments—deepened an already substantial need and opened up new cost-effective funding opportunities.
Diarrhea is one of the leading causes of death for children under five, killing more than 400,000 children every year. GiveWell has funded several programs to prevent and treat diarrhea, and we recently expanded our work by making a grant to support the implementation and evaluation of WASHmobile in the Democratic Republic of the Congo (DRC) and South Sudan. The program, which was developed by Johns Hopkins University, delivers water treatment supplies and hygiene messaging to households at the highest risk of diarrheal disease. It has shown promising results in randomized controlled trials (RCTs) but hasn’t yet been implemented at scale in the challenging settings where the need is greatest.
GiveWell tends to be known for its work in areas like malaria prevention, water treatment, and nutrition. But as our research team has grown, so has our ability to search for cost-effective programs across a much wider range of issues.