A general method for causal effect estimation under unknown network dependence by combining structure learning and interference modeling, shown on synthetic datasets.
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Survey of algebraic statistics applications to network models for relational data, causal structure discovery, and phylogenetics, emphasizing statistical achievements and practical relevance.
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Causal Inference Under Interference And Network Uncertainty
A general method for causal effect estimation under unknown network dependence by combining structure learning and interference modeling, shown on synthetic datasets.
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Algebraic Statistics in Practice: Applications to Networks
Survey of algebraic statistics applications to network models for relational data, causal structure discovery, and phylogenetics, emphasizing statistical achievements and practical relevance.