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Publication: Berlin: Ernst Siegfried Mittler und Sohn, 1910
URN: urn:nbn:de:kobv:109-1-15487423
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Journal of causal inference

Periodical

Title:
Journal of causal inference
Publication:
Berlin: ˜deœ Gruyter
Note:
Gesehen am 28.07.14
Open Access
Namensnennung 4.0 International
C!z LF gelöscht(28-07-14)
Scope:
Online-Ressource
ISSN:
2193-3685
ZDB-ID:
2742570-8 ZDB
Keywords:
Zeitschrift
Classification:
Mathematik
Wirtschaft
Collection:
Mathematik
Wirtschaft
Copyright:
Rights reserved
Accessibility:
Free Access
Title:
Journal of causal inference
Publication:
Berlin: ˜deœ Gruyter
Note:
Gesehen am 28.07.14
Open Access
Namensnennung 4.0 International
C!z LF gelöscht(28-07-14)
Scope:
Online-Ressource
ISSN:
2193-3685
ZDB-ID:
2742570-8 ZDB
Keywords:
Zeitschrift
Classification:
Mathematik
Wirtschaft
Collection:
Mathematik
Wirtschaft
Copyright:
Rights reserved
Accessibility:
Free Access

Conservative inference for counterfactuals

Article

Author:
Balakrishnan, Sivaraman
Kennedy, Edward
Wasserman, Larry
Title:
Conservative inference for counterfactuals
Publication:
Berlin: ˜deœ Gruyter, 2025
Language:
English
Information:
Abstract: In causal inference, the joint law of a set of counterfactual random variables is generally not identified. But many interesting quantities are functions of the joint distribution. For example, the individual treatment effect is a difference of counterfactuals and any functional of this difference such as the variance, the quantiles and density, all depend on this joint distribution. For binary treatments, many researchers have found identifiable bounds on these quantities. We extend this idea to continuous treatments. We show that a conservative version of the joint law – corresponding to the smallest treatment effect – is identified. The notion of “conservative” depends on how we choose to measure the causal effect and we consider a few such measures. Finding this law uses recent results from optimal transport theory. Under this conservative law we can bound causal effects and we may construct inferences for each individual’s counterfactual dose-response curve. Intuitively, this is the flattest counterfactual curve for each subject that is consistent with the distribution of the observables. If the outcome is univariate then, under mild conditions, this curve is simply the quantile function of the counterfactual distribution that passes through the observed point. This curve corresponds to a nonparametric rank preserving structural model.
Scope:
Online-Ressource
Note:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Keywords:
counterfactual ; bounds ; transport ; 62G05 ; 62G08
Classification:
Mathematik
Wirtschaft
Sonstiges
URN:
urn:nbn:de:101:1-2504020635478.897562899983
Collection:
Mathematik
Wirtschaft
Sonstiges
Copyright:
CC BY
Accessibility:
Free Access
Author:
Balakrishnan, Sivaraman
Kennedy, Edward
Wasserman, Larry
Title:
Conservative inference for counterfactuals
Publication:
Berlin: ˜deœ Gruyter, 2025
Language:
English
Information:
Abstract: In causal inference, the joint law of a set of counterfactual random variables is generally not identified. But many interesting quantities are functions of the joint distribution. For example, the individual treatment effect is a difference of counterfactuals and any functional of this difference such as the variance, the quantiles and density, all depend on this joint distribution. For binary treatments, many researchers have found identifiable bounds on these quantities. We extend this idea to continuous treatments. We show that a conservative version of the joint law – corresponding to the smallest treatment effect – is identified. The notion of “conservative” depends on how we choose to measure the causal effect and we consider a few such measures. Finding this law uses recent results from optimal transport theory. Under this conservative law we can bound causal effects and we may construct inferences for each individual’s counterfactual dose-response curve. Intuitively, this is the flattest counterfactual curve for each subject that is consistent with the distribution of the observables. If the outcome is univariate then, under mild conditions, this curve is simply the quantile function of the counterfactual distribution that passes through the observed point. This curve corresponds to a nonparametric rank preserving structural model.
Scope:
Online-Ressource
Note:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Keywords:
counterfactual ; bounds ; transport ; 62G05 ; 62G08
Classification:
Mathematik
Wirtschaft
Sonstiges
URN:
urn:nbn:de:101:1-2504020635478.897562899983
Collection:
Mathematik
Wirtschaft
Sonstiges
Copyright:
CC BY
Accessibility:
Free Access
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