Giving Compass' Take:
- Rebecca Nissan explains the potential of machine learning to advance the field of behavioral science and facilitate better outcomes for interventions in heterogenous populations.
- Why are science and technology important to the philanthropy sector? How can you use insights from outside fields to guide your giving and increase your impact?
- Read about using machine learning to predict wildfire damage.
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Innovative machine learning methods for estimating heterogeneous treatment effects (for example, causal forests) enable behavioral scientists to strategically deliver interventions to those most likely to benefit from them. Crucially, these methods also reveal who is not likely to benefit from an intervention, allowing us to design alternative solutions that will work for these individuals and meet the diverse needs of a heterogeneous population. Existing methods do allow us to explore differences in response to treatment between pre-specified subgroups, but they do not capture the complex and intertwined factors that truly drive variation in how people respond to interventions.
In 2019, with support from Schmidt Futures and The Alfred P. Sloan Foundation, ideas42 collaborated with the Golub Capital Social Impact Lab at Stanford University to apply machine learning methods to a set of real-world behavioral interventions. We chose a set of prior ideas42 experiments that had positive average treatment effects (we knew they worked well, on average), and set out to explore heterogeneous treatment effects (for whom do these interventions work?). Ultimately, the goal is to use information about how different people respond to treatment to make strategic decisions about who should receive an intervention in the future.
The culmination of our first year of work is the new report: Computational Applications to Behavioral Science. It is designed to be a guide for researchers, policy-makers, and practitioners who want to use machine learning to enhance their own field experiments – with a particular focus on behavioral science interventions. The report includes technical descriptions and tutorials for how to implement these methods, two illustrative case studies, and reflections on which experimental contexts are best suited for this particular application of machine learning.
Read the full article about machine learning by Rebecca Nissan at ideas42.