Identification of Conditional Causal Effects under Markov Equivalence

Amin JABER, Jiji ZHANG, Elias Bareinboim

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

Abstract

Causal identification is the problem of deciding whether a post-interventional
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 languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings
EditorsH. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett
PublisherNeural Information Processing Systems Foundation
Publication statusPublished - Dec 2019
EventThe 33rd annual Conference on Neural Information Processing Systems - Vancouver Convention Center, Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019
https://nips.cc/Conferences/2019

Conference

ConferenceThe 33rd annual Conference on Neural Information Processing Systems
Abbreviated titleNeurIPS 2019
CountryCanada
CityVancouver
Period8/12/1914/12/19
Internet address

Fingerprint

Equivalence
Diagrams
Causal effect
Data generating process

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

JABER, A., ZHANG, J., & Bareinboim, E. (2019). Identification of Conditional Causal Effects under Markov Equivalence. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings Neural Information Processing Systems Foundation.
JABER, Amin ; ZHANG, Jiji ; Bareinboim, Elias. / Identification of Conditional Causal Effects under Markov Equivalence. Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings. editor / H. Wallach ; H. Larochelle ; A. Beygelzimer ; F. d'Alché-Buc ; E. Fox ; R. Garnett. Neural Information Processing Systems Foundation, 2019.
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abstract = "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.",
author = "Amin JABER and Jiji ZHANG and Elias Bareinboim",
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JABER, A, ZHANG, J & Bareinboim, E 2019, Identification of Conditional Causal Effects under Markov Equivalence. in H Wallach, H Larochelle, A Beygelzimer, F d'Alché-Buc, E Fox & R Garnett (eds), 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.

Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings. ed. / H. Wallach; H. Larochelle; A. Beygelzimer; F. d'Alché-Buc; E. Fox; R. Garnett. Neural Information Processing Systems Foundation, 2019.

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

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PY - 2019/12

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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.

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JABER A, ZHANG J, Bareinboim E. Identification of Conditional Causal Effects under Markov Equivalence. In Wallach H, Larochelle H, Beygelzimer A, d'Alché-Buc F, Fox E, Garnett R, editors, Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings. Neural Information Processing Systems Foundation. 2019