Identifiability Criteria for Causal Effects of Joint Interventions

Kuroki, Masahide; Miyakawa, Masami

doi:10.14490/jjss1995.29.105

Cited by 33 publications

(20 citation statements)

References 5 publications

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“…Applications of this calculus to problems involving multiple interventions (e.g., time varying treatments), conditional policies, and surrogate experiments were developed in Pearl and Robins (1995), Kuroki and Miyakawa (1999), and Pearl (2000a, Chapters 3-4).…”

Section: General Control Of Confoundingmentioning

confidence: 99%

An Introduction to Causal Inference

Pearl¹

2010

The International Journal of Biostatistics

455

312

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This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

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Section: General Control Of Confoundingmentioning

confidence: 99%

An Introduction to Causal Inference

Pearl¹

2010

The International Journal of Biostatistics

455

312

View full text Add to dashboard Cite

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“…Further study can be also found in Kuroki and Miyakawa (1999) and Robins (1997). More recently, Tian and Pearl published a series of papers related to this topic (Tian and Pearl 2002;Tian and Pearl 2002;Tian and Pearl 380 2003).…”

mentioning

confidence: 97%

Identifiability in Causal Bayesian Networks: A Gentle Introduction

Valtorta

Huang

2008

Cybernetics and Systems

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“…4) and extends previous analysis and algorithms of identifiability given in [Pearl, 1995;Kuroki and Miyakawa, 1999;Tian and Pearl, 2002;Shpitser and Pearl, 2006b;Huang and Valtorta, 2006]. We build on two observations developed along the paper: (i) Transportability: Causal relations can can be partitioned into trivially and directly transportable.…”

Section: A Complete Algorithm For Transportability Of Joint Effectsmentioning

confidence: 74%

Transportability of Causal Effects: Completeness Results

Bareinboim

Pearl

2012

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The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal conditions for such transfer but falls short of providing an effective procedure for deciding whether transportability is feasible for a given set of assumptions about differences between the source and target domains. This paper provides such procedure. It establishes a necessary and sufficient condition for deciding when causal effects in the target domain are estimable from both the statistical information available and the causal information transferred from the experiments. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an estimate of the desired causal relation.

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Identifiability Criteria for Causal Effects of Joint Interventions

Cited by 33 publications

References 5 publications

An Introduction to Causal Inference

An Introduction to Causal Inference

Identifiability in Causal Bayesian Networks: A Gentle Introduction

Transportability of Causal Effects: Completeness Results

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