A still better performance guarantee for approximate graph coloring
Information Processing Letters
Completely separating systems of k-sets
Discrete Mathematics
Minimal completely separating systems of k-sets
Journal of Combinatorial Theory Series A
On the Approximability of the Minimum Test Collection Problem
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
On Exact and Approximate Cut Covers of Graphs
On Exact and Approximate Cut Covers of Graphs
Combinatorica
Maximum directed cuts in acyclic digraphs
Journal of Graph Theory
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Minimal cut cover of a graph with an application to the testing of electronic boards
Operations Research Letters
Learning linear cyclic causal models with latent variables
The Journal of Machine Learning Research
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Randomized controlled experiments are often described as the most reliable tool available to scientists for discovering causal relationships among quantities of interest. However, it is often unclear how many and which different experiments are needed to identify the full (possibly cyclic) causal structure among some given (possibly causally insufficient) set of variables. Recent results in the causal discovery literature have explored various identifiability criteria that depend on the assumptions one is able to make about the underlying causal process, but these criteria are not directly constructive for selecting the optimal set of experiments. Fortunately, many of the needed constructions already exist in the combinatorics literature, albeit under terminology which is unfamiliar to most of the causal discovery community. In this paper we translate the theoretical results and apply them to the concrete problem of experiment selection. For a variety of settings we give explicit constructions of the optimal set of experiments and adapt some of the general combinatorics results to answer questions relating to the problem of experiment selection.