SADA: A General Framework to Support Robust Causation Discovery

@inproceedings{Cai2013SADAAG,
  title={SADA: A General Framework to Support Robust Causation Discovery},
  author={Ruichu Cai and Zhenjie Zhang and Zhifeng Hao},
  booktitle={ICML},
  year={2013}
}
Causality discovery without manipulation is considered a crucial problem to a variety of applications, such as genetic therapy. The state-of-the-art solutions, e.g. LiNGAM, return accurate results when the number of labeled samples is larger than the number of variables. These approaches are thus applicable only when large numbers of samples are available or the problem domain is sufficiently small. Motivated by the observations of the local sparsity properties on causal structures, we propose… CONTINUE READING
Highly Cited
This paper has 32 citations. REVIEW CITATIONS

8 Figures & Tables

Topics

Statistics

01020201620172018
Citations per Year

Citation Velocity: 9

Averaging 9 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.