| Researchers developed a new active learningor machine learningstrategy that outperformed existing approaches for identifying optimal interventions when designing causal models. The new approach, which was developed by researchers from Massachusetts Institute of Technology and Harvard University, was recently described in a paper inNature Machine Intelligence. The research was partially funded by the National Center for Complementary and Integrative Health. Identifying interventions that can be applied to a system to produce a desired outcome is a challenge across many disciplines, including science, engineering, and public policy. Not knowing much about an outcome before implementing an intervention can mean extensive options for the intervention design, and doing an exhaustive search for the optimal design may not be feasible. In such cases, when the number of interventions is large, experimental design strategies are needed to help identify desirable interventions more efficiently. | 
