Corpus ID: 202779238

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

@inproceedings{Gentzel2019TheCF,
  title={The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data},
  author={Amanda Gentzel and Dan Garant and David. D. Jensen},
  booktitle={NeurIPS},
  year={2019}
}
  • Amanda Gentzel, Dan Garant, David. D. Jensen
  • Published in NeurIPS 2019
  • Computer Science, Mathematics
  • Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies… CONTINUE READING

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    SHOWING 1-10 OF 31 REFERENCES

    Optimal Structure Identification With Greedy Search

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Divergence measures based on the Shannon entropy

    • Jianhua Lin
    • Computer Science, Mathematics
    • IEEE Trans. Inf. Theory
    • 1991
    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Structural Intervention Distance for Evaluating Causal Graphs

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL