Causal Effect Identification in a Sub-Population with Latent Variables

2024/05/23 by Amir Mohammad Abouei, Abouei, Amir Mohammad, Ehsan Mokhtarian +5
Mathematics · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2405.14547

openalex publication_date 2024/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 (Abouei et al., 2023). 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 (Pearl, 1995; Tian & Pearl, 2002), to their new counterparts. Subsequently, we propose a sound algorithm for the s-ID problem with latent variables.

Related

• s-ID: Causal Effect Identification in a Sub-Population
• Causal Effect Identification with Context-specific Independence\n Relations of Control Variables
• The Proximal ID Algorithm
• On Identifiability of Conditional Causal Effects
• Causal Effect Identification in LiNGAM Models with Latent Confounders