Causal Effect Identification in a Sub-Population with Latent Variables
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Abstract (arXiv)
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.
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References · 11
- 2021Nested Counterfactual Identification from Arbitrary Surrogate Experimentscited by 38
- 2019Causal Inference Under Interference And Network Uncertaintycited by 27
- 2020Identifying Causal Effects via Context-specific Independence Relationscited by 21
- 2019Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approachcited by 16
- 2022Revisiting the General Identifiability Problemcited by 13
- 2019Identification and Estimation of Causal Effects from Dependent Datacited by 8
- 2021Causal Effect Identification with Context-specific Independence Relations of Control Variablescited by 8
- 2023Causal Imitability Under Context-Specific Independence Relationscited by 6
- 2024A General Identification Algorithm For Data Fusion Problems Under Systematic Selectioncited by 5
- 2023On Identifiability of Conditional Causal Effectscited by 4
- 2023s-ID: Causal Effect Identification in a Sub-Populationcited by 3
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