The study focuses on testing causal relationships within Markov equivalence classes. It introduces constraint-based methods to address the complexity of conditional independence tests, providing insights into learning versus testing problems in causal discovery. The work establishes lower and upper bounds on the number of CI tests needed for accurate testing, offering implications for future research directions.
Understanding causal relationships is crucial across various scientific fields, with extensive research dedicated to learning causal graphs from data. However, the complementary concept of testing specific aspects of hidden causal graphs remains underexplored. The study introduces novel constraint-based methods to explore the complexities of conditional independence tests in the context of causal discovery.
The research highlights that testing is a more manageable task compared to learning, requiring exponentially fewer independence tests in certain graph structures. By establishing lower and upper bounds on the number of CI tests needed for accurate testing, the study provides valuable insights into the complexity of verifying pre-defined causal relationships within equivalence classes.
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arxiv.org
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