The authors address a resource allocation problem in causal inference: when you can only partially identify a causal effect from observational data (meaning you get bounds, not a point estimate), which experiments should you run to tighten those bounds most effectively given a budget? They formalize this as the "max-potency" problem, where potency measures the worst-case guaranteed improvement in bound width, and show it's computationally hard (NP-hard via reduction from knapsack). They introduce two graphical pruning rules that together eliminate 50-88% of candidate experiments without any computation, and demonstrate the approach on health data (selecting experiments to estimate physical activity's effect on diabetes).
Main takeaways:
- When causal effects are only partially identifiable (you get bounds, not exact values), choosing which experiments to run to tighten those bounds is NP-hard
- "Epistemic potency" measures how much an experiment is guaranteed to narrow the bounds in the worst case
- Two pruning rules based on the causal graph structure alone can eliminate most useless experiments before doing expensive computation: a path-interception rule and an identifiability check
- On benchmark causal graphs, these rules prune 50-88% of candidate experiments on average
- The framework is general enough to apply to real observational datasets (demonstrated on health survey data)