Cost-effectiveness analysis (CEA) is widely used in philanthropy, policy, and nonprofit work to allocate resources for maximum impact. However, current models often fall short in comparing interventions across different cause areas, rely on rigid assumptions, and struggle to account for uncertainty or hard-to-quantify impacts. This creates a gap where decision-makers lack tools to systematically evaluate trade-offs between diverse interventions.

Expanding Cost-Effectiveness Analysis

One way to address these limitations could be by developing a more flexible and comprehensive cross-cause cost-effectiveness model. This might involve:

• Creating tailored frameworks for different cause areas (global health, animal welfare, existential risk) while allowing comparisons between them
• Incorporating adjustable parameters for key assumptions like discount rates and moral weights
• Using probabilistic methods to better represent uncertainty in outcomes
• Developing new metrics for impacts that are difficult to quantify in traditional terms

Such an approach could help funders, policymakers and researchers make more informed decisions when allocating resources across different types of interventions.

Implementation and Potential Impact

For execution, a phased approach might work best:

1. Start with a minimal version focusing on one new cause area while maintaining the existing framework
2. Gradually add features like uncertainty modeling and cross-cause comparison tools
3. Eventually incorporate more experimental metrics and collaborative features

The tool could be particularly valuable for:

• Philanthropic organizations making funding decisions across multiple causes
• Policy analysts working on global priorities
• Nonprofits seeking to demonstrate their impact
• Researchers studying effectiveness across different domains

While existing models excel within specific domains, this approach could fill an important gap by enabling systematic comparisons across different types of interventions, with appropriate flexibility and transparency about assumptions.