NP-Hard Problem of Optimal Experiment Selection for Causal Bounds
Researchers formalized the max-potency problem for selecting cost-constrained experiments that maximally tighten partial causal identification bounds. The problem is proven NP-hard via reduction from 0-1 knapsack. Building on Duarte et al. (2023), a general procedure evaluates epistemic potency in discrete settings. Two graphical pruning criteria, including a novel path-interception rule exploiting district structure, reduce the super-exponential search space. The work appears on arXiv (2605.06993v1).
Key facts
- Causal queries are often partially identifiable from observational data.
- Experiments can tighten bounds but are costly.
- Max-potency problem selects experiments to maximally tighten bounds under cost constraints.
- Epistemic potency measures worst-case reduction in bound width.
- Problem is NP-hard via reduction from 0-1 knapsack.
- General procedure for evaluating epistemic potency in discrete settings from Duarte et al. (2023).
- Two graphical pruning criteria introduced: path-interception rule and district structure.
- Path-interception rule certifies zero potency in linear time.
- Paper on arXiv: 2605.06993v1.
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Institutions
- arXiv