Optimal Experiments for Partial Causal Effect Identification
Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically costly. We study the problem of selecting, prior to observing experimental outcomes, a cost-constrained subset of experiments that maximally tightens bounds on a target query. We formalize this as the max-potency problem, where epistemic potency measures the worst-case reduction in bound width guaranteed by an experiment, and show that this problem is NP-hard via a reduction from 0-1 knapsack. Building on the polynomial-programming framework of Duarte et al. (2023), we give a general procedure for evaluating epistemic potency in discrete setti...
| Authors | Tobias Maringgele, Jalal Etesami |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 5 September 2026 |
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