Varieties of Causal Intervention

Korb, Kevin B.; Hope, Lucas R.; Nicholson, Ann E.; Axnick, Karl

doi:10.1007/978-3-540-28633-2_35

Cited by 71 publications

(47 citation statements)

References 6 publications

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“…This intervention has two consequences on the distribution of the exposure: (i) it changes the distribution of values of A below δ 1 from g 0 to g I and (ii) it relocates the values of A above δ 1 between δ 1 and δ 2 according to distribution (5). As special case, we will consider the case g I ¼ g 0 , which is of particular interest when we want to assess the effect of policies that enforce the value of certain exposure below a pre-specified level.…”

Section: Causal and Statistical Parametersmentioning

confidence: 99%

“…Under such a policy, individuals polluting above the threshold will only have an incentive to reduce their pollution levels to a value that is in accordance with the policy, having no further incentive to go below the enforced cutoff point once they have reached it. Therefore, the most likely postintervention distribution for this policy is one that locates the probability mass associated to the noncompliers around the cutoff point, i.e., intervention (5). The use of intervention (12) (3) skewed to the right, and (4) symmetric U-shaped.…”

Section: Comparison With the Standard Approachmentioning

confidence: 99%

“…A static intervention is one in which the treatment is set to a given fixed value deterministically, while a dynamic intervention allows such value to depend on variables that precede it causally. Static interventions have also been called deterministic [5] or atomic [1].…”

Section: Introductionmentioning

confidence: 99%

“…In general [5], an intervention can be simply defined as an external manipulation of a causal system, whether that manipulation is deterministic or stochastic. A static intervention corresponds to an alteration of the causal system in which the density of the exposure is changed to a degenerate one.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Díaz

Laan

2013

The International Journal of Biostatistics

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Assessing the causal effect of an exposure often involves the definition of counterfactual outcomes in a hypothetical world in which the stochastic nature of the exposure is modified. Although stochastic interventions are a powerful tool to measure the causal effect of a realistic intervention that intends to alter the population distribution of an exposure, their importance to answer questions about plausible policy interventions has been obscured by the generalized use of deterministic interventions. In this article, we follow the approach described in Díaz and van der Laan (2012) to define and estimate the effect of an intervention that is expected to cause a truncation in the population distribution of the exposure. The observed data parameter that identifies the causal parameter of interest is established, as well as its efficient influence function under the non-parametric model. Inverse probability of treatment weighted (IPTW), augmented IPTW and targeted minimum loss-based estimators (TMLE) are proposed, their consistency and efficiency properties are determined. An extension to longitudinal data structures is presented and its use is demonstrated with a real data example.

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Section: Causal and Statistical Parametersmentioning

confidence: 99%

Section: Comparison With the Standard Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Díaz

Laan

2013

The International Journal of Biostatistics

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“…Such "perfect" interventions have been variously referred to as "atomic" [2], "strong" [18], "structural" [19], or "independent" [20] interventions. This type of intervention also provides the basis for Pearl's "do-calculus" [2].…”

Section: Part 1: Observations and Interventionsmentioning

confidence: 99%

Observing and Intervening: Rational and Heuristic Models of Causal Decision Making

Meder¹,

Hagmayer²,

Waldmann³

2010

TOPSYJ

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Abstract:Recently, a number of rational theories have been put forward which provide a coherent formal framework for modeling different types of causal inferences, such as prediction, diagnosis, and action planning. A hallmark of these theories is their capacity to simultaneously express probability distributions under observational and interventional scenarios, thereby rendering it possible to derive precise predictions about interventions ("doing") from passive observations ("seeing"). In Part 1 of the paper we discuss different modeling approaches for formally representing interventions and review the empirical evidence on how humans draw causal inferences based on observations or interventions. We contrast deterministic interventions with imperfect actions yielding unreliable or unknown outcomes. In Part 2, we discuss alternative strategies for making interventional decisions when the causal structure is unknown to the agent. A Bayesian approach of rational causal inference, which aims to infer the structure and its parameters from the available data, provides the benchmark model. This account is contrasted with a heuristic approach which knows categories of causes and effects but neglects further structural information. The results of computer simulations show that despite its computational parsimony the heuristic approach achieves very good performance compared to the Bayesian model.

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Student Modeling

Sucar¹,

Noguez²

2008

Bayesian Networks

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Statistics in Practice is an important international series of texts which provide detailed coverage of statistical concepts, methods and worked case studies in specific fields of investigation and study.With sound motivation and many worked practical examples, the books show in down-to-earth terms how to select and use an appropriate range of statistical techniques in a particular practical field within each title's special topic area.The books provide statistical support for professionals and research workers across a range of employment fields and research environments. Subject areas covered include medicine and pharmaceutics; industry, finance and commerce; public services; the earth and environmental sciences, and so on.The books also provide support to students studying statistical courses applied to the above areas. The demand for graduates to be equipped for the work environment has led to such courses becoming increasingly prevalent at universities and colleges.It is our aim to present judiciously chosen and well-written workbooks to meet everyday practical needs. Feedback of views from readers will be most valuable to monitor the success of this aim.

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Varieties of Causal Intervention

Cited by 71 publications

References 6 publications

Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Observing and Intervening: Rational and Heuristic Models of Causal Decision Making

Student Modeling

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