## Abstract

We consider the problem of conditional independence testing of X and Y given Z where X, Y and Z are three real random variables and Z is continuous. We focus on two main cases-when X and Y are both discrete, and when X and Y are both continuous. In view of recent results on conditional independence testing [Ann. Statist. 48 (2020) 1514-1538], one cannot hope to design nontrivial tests, which control the type I error for all absolutely continuous conditionally independent distributions, while still ensuring power against interesting alternatives. Consequently, we identify various, natural smoothness assumptions on the conditional distributions of X, Y |Z = z as z varies in the support of Z, and study the hardness of conditional independence testing under these smoothness assumptions. We derive matching lower and upper bounds on the critical radius of separation between the null and alternative hypotheses in the total variation metric. The tests we consider are easily implementable and rely on binning the support of the continuous variable Z. To complement these results, we provide a new proof of the hardness result of Shah and Peters [Ann. Statist. 48 (2020) 1514-1538].

Original language | English (US) |
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Pages (from-to) | 2151-2177 |

Number of pages | 27 |

Journal | Annals of Statistics |

Volume | 49 |

Issue number | 4 |

DOIs | |

State | Published - Aug 2021 |

### Funding

Acknowledgments. The authors would like to thank the editor, associate editor and two anonymous referees whose comments and constructive suggestions led to significant improvements of the manuscript. The authors are also grateful to Ilmun Kim for various discussions on the topic. The second author was partially supported by NSF DMS17130003, NSF CCF1763734. The third author was supported by NSF DMS1713003.

## Keywords

- Conditional independence
- Hypothesis testing
- Minimax optimality

## ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty