Optimal Experiments for Partial Causal Effect Identification
Quick summary
arXiv:2605.06993v2 Announce Type: replace Abstract: 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. Bui
Key takeaways
- arXiv:2605.06993v2 Announce Type: replace Abstract: 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.
Why it matters
“Optimal Experiments for Partial Causal Effect Identification” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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