Publication

Causal Effect Identification in a Sub-population with Latent Variables

2024

Abstract

The S-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub-population [AMK24]. This problem has been addressed when all the variables in the system are observable. In this paper, we consider an extension of the S-ID problem that allows for the presence of latent variables. To tackle the challenges induced by the presence of latent variables in a sub-population, we first extend the classical relevant graphical definitions, such as C-components and Hedges, initially defined for the so-called ID problem [Pea95, TP02], to their new counterparts. Subsequently, we propose a sound algorithm for the S-ID problem with latent variables.

Official source

https://infoscience.epfl.ch/entities/publication/8c46130d-e466-4973-8778-1d86e844ca41

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2024

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8c46130d-e466-4973-8778-1d86e844ca41

About this result

Related concepts (29)

Causal inference

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Population ecology

Population ecology is a sub-field of ecology that deals with the dynamics of species populations and how these populations interact with the environment, such as birth and death rates, and by immigration and emigration. The discipline is important in conservation biology, especially in the development of population viability analysis which makes it possible to predict the long-term probability of a species persisting in a given patch of habitat.

Causal model

In the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial.

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Causal Effect Identification in a Sub-population with Latent Variables

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