### Abstract

distribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagram, and an observational distribution. A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution. Recent work by (Jaber et al., 2019a) devised a complete algorithm for the identification of unconditional causal effects given a Markov equivalence class of causal diagrams. However, there are identifiable conditional causal effects that cannot be handled by that algorithm. In this work, we derive an algorithm to identify conditional effects, which are particularly useful for evaluating conditional plans or policies.

Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings |

Editors | H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett |

Publisher | Neural Information Processing Systems Foundation |

Publication status | Published - Dec 2019 |

Event | The 33rd annual Conference on Neural Information Processing Systems - Vancouver Convention Center, Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 https://nips.cc/Conferences/2019 |

### Conference

Conference | The 33rd annual Conference on Neural Information Processing Systems |
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Abbreviated title | NeurIPS 2019 |

Country | Canada |

City | Vancouver |

Period | 8/12/19 → 14/12/19 |

Internet address |

### Fingerprint

### Bibliographical note

Bareinboim and Jaber are supported in parts by grants from NSF IIS-1704352, IIS-1750807 (CAREER), IBM Research, and Adobe Research. Zhang’s research was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU13602818.### Cite this

*Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings*Neural Information Processing Systems Foundation.

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*Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings.*Neural Information Processing Systems Foundation, The 33rd annual Conference on Neural Information Processing Systems, Vancouver, Canada, 8/12/19.

**Identification of Conditional Causal Effects under Markov Equivalence.** / JABER, Amin; ZHANG, Jiji; Bareinboim, Elias.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed)

TY - GEN

T1 - Identification of Conditional Causal Effects under Markov Equivalence

AU - JABER, Amin

AU - ZHANG, Jiji

AU - Bareinboim, Elias

N1 - Bareinboim and Jaber are supported in parts by grants from NSF IIS-1704352, IIS-1750807 (CAREER), IBM Research, and Adobe Research. Zhang’s research was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU13602818.

PY - 2019/12

Y1 - 2019/12

N2 - Causal identification is the problem of deciding whether a post-interventionaldistribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagram, and an observational distribution. A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution. Recent work by (Jaber et al., 2019a) devised a complete algorithm for the identification of unconditional causal effects given a Markov equivalence class of causal diagrams. However, there are identifiable conditional causal effects that cannot be handled by that algorithm. In this work, we derive an algorithm to identify conditional effects, which are particularly useful for evaluating conditional plans or policies.

AB - Causal identification is the problem of deciding whether a post-interventionaldistribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagram, and an observational distribution. A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution. Recent work by (Jaber et al., 2019a) devised a complete algorithm for the identification of unconditional causal effects given a Markov equivalence class of causal diagrams. However, there are identifiable conditional causal effects that cannot be handled by that algorithm. In this work, we derive an algorithm to identify conditional effects, which are particularly useful for evaluating conditional plans or policies.

M3 - Conference paper (refereed)

BT - Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings

A2 - Wallach, H.

A2 - Larochelle, H.

A2 - Beygelzimer, A.

A2 - d'Alché-Buc, F.

A2 - Fox, E.

A2 - Garnett, R.

PB - Neural Information Processing Systems Foundation

ER -