Identification of Conditional Causal Effects under Markov Equivalence

Amin JABER; Jiji ZHANG; Elias BAREINBOIM

Identification of Conditional Causal Effects under Markov Equivalence

Amin JABER, Jiji ZHANG, Elias BAREINBOIM

Lingnan University

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

7 Citations (Scopus)

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 language	English
Title of host publication	Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Editors	Hanna M. WALLACH, Hugo LAROCHELLE, Alina BEYGELZIMER, Florence D'ALCHÉ-BUC, Edward A. FOX, Roman GARNETT
Publisher	Neural Information Processing Systems Foundation
Number of pages	9
ISBN (Print)	9781713807933
Publication status	Published - Dec 2019
Event	33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver Convention Center, Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 https://nips.cc/Conferences/2019

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Conference

Conference	33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019
Abbreviated title	NeurIPS 2019
Country/Territory	Canada
City	Vancouver
Period	8/12/19 → 14/12/19
Internet address	https://nips.cc/Conferences/2019

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.

Access to Document

http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence

Cite this

JABER, A., ZHANG, J., & BAREINBOIM, E. (2019). Identification of Conditional Causal Effects under Markov Equivalence. In H. M. WALLACH, H. LAROCHELLE, A. BEYGELZIMER, F. D'ALCHÉ-BUC, E. A. FOX, & R. GARNETT (Eds.), Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (Advances in Neural Information Processing Systems). Neural Information Processing Systems Foundation. http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence

JABER, Amin ; ZHANG, Jiji ; BAREINBOIM, Elias. / Identification of Conditional Causal Effects under Markov Equivalence. Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). editor / Hanna M. WALLACH ; Hugo LAROCHELLE ; Alina BEYGELZIMER ; Florence D'ALCHÉ-BUC ; Edward A. FOX ; Roman GARNETT. Neural Information Processing Systems Foundation, 2019. (Advances in Neural Information Processing Systems).

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title = "Identification of Conditional Causal Effects under Markov Equivalence",

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",

note = "Bareinboim and Jaber are supported in parts by grants from NSF IIS-1704352, IIS-1750807 (CAREER), IBM Research, and Adobe Research. Zhang{\textquoteright}s research was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU13602818.; 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, NeurIPS 2019 ; Conference date: 08-12-2019 Through 14-12-2019",

year = "2019",

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language = "English",

isbn = "9781713807933",

series = "Advances in Neural Information Processing Systems",

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JABER, A, ZHANG, J & BAREINBOIM, E 2019, Identification of Conditional Causal Effects under Markov Equivalence. in HM WALLACH, H LAROCHELLE, A BEYGELZIMER, F D'ALCHÉ-BUC, EA FOX & R GARNETT (eds), Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, Vancouver, British Columbia, Canada, 8/12/19. <http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence>

Identification of Conditional Causal Effects under Markov Equivalence. / JABER, Amin; ZHANG, Jiji; BAREINBOIM, Elias.
Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). ed. / Hanna M. WALLACH; Hugo LAROCHELLE; Alina BEYGELZIMER; Florence D'ALCHÉ-BUC; Edward A. FOX; Roman GARNETT. Neural Information Processing Systems Foundation, 2019. (Advances in Neural Information Processing Systems).

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

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.

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M3 - Conference paper (refereed)

AN - SCOPUS:85090169534

SN - 9781713807933

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

A2 - WALLACH, Hanna M.

A2 - LAROCHELLE, Hugo

A2 - BEYGELZIMER, Alina

A2 - D'ALCHÉ-BUC, Florence

A2 - FOX, Edward A.

A2 - GARNETT, Roman

PB - Neural Information Processing Systems Foundation

T2 - 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

Y2 - 8 December 2019 through 14 December 2019

ER -

JABER A, ZHANG J, BAREINBOIM E. Identification of Conditional Causal Effects under Markov Equivalence. In WALLACH HM, LAROCHELLE H, BEYGELZIMER A, D'ALCHÉ-BUC F, FOX EA, GARNETT R, editors, Advances in Neural Information Processing Systems, 32 : 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Neural Information Processing Systems Foundation. 2019. (Advances in Neural Information Processing Systems).

Identification of Conditional Causal Effects under Markov Equivalence

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Logical Investigations of Causal Models and Counterfactual Structures (因果模型與反實結構的邏輯探究)

Causation, Decision, and Imprecise Probabilities

Cite this

Identification of Conditional Causal Effects under Markov Equivalence

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Projects

Logical Investigations of Causal Models and Counterfactual Structures (因果模型與反實結構的邏輯探究)

Causation, Decision, and Imprecise Probabilities

Cite this