Budgeted Experiment Design for Causal Structure Learning
This work addresses the challenge of efficient causal discovery for researchers with constrained experimental resources, offering a practical solution with theoretical guarantees, though it is incremental in improving existing methods.
The paper tackles the problem of learning causal structures with a limited budget of non-adaptive experiments, formulating it as an optimization to maximize resolved edge directions, and shows that a greedy algorithm achieves a (1-1/e)-approximation with significant speedup and validates it on synthetic and real graphs, orienting most edges with few interventions.
We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$. We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolved. We prove that the corresponding objective function is submodular and a greedy algorithm suffices to achieve $(1-\frac{1}{e})$-approximation of the optimal value. We further present an accelerated variant of the greedy algorithm, which can lead to orders of magnitude performance speedup. We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions.