Methods: Overview, resources
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"Qualitative" design issues: How to design the 'content' of experiments and surveys to have internal validity and external generalizability
Real-world assignment & inference: How to set up trials to have comparable groups
Adaptive design/sampling, reinforcement learning: Adjusting the treatments and design as you learn, to 'get to the highest value in the end'
Analysis: Statistical approaches: How to make inferences from the data after you have it (and plan this in advance)
Rethink Priorities notes (some are works in progress)...
The https://declaredesign.org/ framework and R package seems very helpful. I (David Reinstein) am learning and trying to adapt it.