Identifiability of path-specific effects

Chen Avin; Ilya Shpitser; Judea Pearl

Identifiability of path-specific effects

Chen Avin, Ilya Shpitser, Judea Pearl

Research output: Contribution to journal › Conference article › peer-review

223 Scopus citations

Abstract

Counterfactual quantities representing path-specific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [Pearl, 2001] details a method by which such an exclusion can be specified formally by fixing the value of the parent node of each excluded edge. In this paper we derive simple, graphical conditions for experimental identifiability of path-specific effects, namely, conditions under which path-specific effects can be estimated consistently from data obtained from controlled experiments.

Original language	English
Pages (from-to)	357-363
Number of pages	7
Journal	IJCAI International Joint Conference on Artificial Intelligence
State	Published - 1 Dec 2005
Externally published	Yes
Event	19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom Duration: 30 Jul 2005 → 5 Aug 2005

ASJC Scopus subject areas

Artificial Intelligence

Cite this

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title = "Identifiability of path-specific effects",

abstract = "Counterfactual quantities representing path-specific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [Pearl, 2001] details a method by which such an exclusion can be specified formally by fixing the value of the parent node of each excluded edge. In this paper we derive simple, graphical conditions for experimental identifiability of path-specific effects, namely, conditions under which path-specific effects can be estimated consistently from data obtained from controlled experiments.",

author = "Chen Avin and Ilya Shpitser and Judea Pearl",

year = "2005",

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language = "English",

pages = "357--363",

journal = "IJCAI International Joint Conference on Artificial Intelligence",

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AU - Avin, Chen

AU - Shpitser, Ilya

AU - Pearl, Judea

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N2 - Counterfactual quantities representing path-specific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [Pearl, 2001] details a method by which such an exclusion can be specified formally by fixing the value of the parent node of each excluded edge. In this paper we derive simple, graphical conditions for experimental identifiability of path-specific effects, namely, conditions under which path-specific effects can be estimated consistently from data obtained from controlled experiments.

AB - Counterfactual quantities representing path-specific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [Pearl, 2001] details a method by which such an exclusion can be specified formally by fixing the value of the parent node of each excluded edge. In this paper we derive simple, graphical conditions for experimental identifiability of path-specific effects, namely, conditions under which path-specific effects can be estimated consistently from data obtained from controlled experiments.

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M3 - Conference article

AN - SCOPUS:84880739159

SN - 1045-0823

SP - 357

EP - 363

JO - IJCAI International Joint Conference on Artificial Intelligence

JF - IJCAI International Joint Conference on Artificial Intelligence

T2 - 19th International Joint Conference on Artificial Intelligence, IJCAI 2005

Y2 - 30 July 2005 through 5 August 2005

ER -

Identifiability of path-specific effects

Abstract

ASJC Scopus subject areas

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