mlsanityData to 14 Oct 2026

s-ID: Causal Effect Identification in a Sub-Population

arXiv 2309.02281 ↗First posted 5 Sep 2023cs.LG cs.AI stat.MEcited by 3

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Abstract (arXiv)

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

11 of 35 references.

References · 11

  1. 2022Latent Hierarchical Causal Structure Discovery with Rank Constraintscited by 51
  2. 2013Learning Sparse Causal Models is not NP-hardcited by 49
  3. 2021Nested Counterfactual Identification from Arbitrary Surrogate Experimentscited by 38
  4. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
  5. 2019Ordering-Based Causal Structure Learning in the Presence of Latent Variablescited by 26
  6. 2020Identifying Causal Effects via Context-specific Independence Relationscited by 22
  7. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  8. 2022Revisiting the General Identifiability Problemcited by 13
  9. 2022Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variablescited by 10
  10. 2021Causal Effect Identification with Context-specific Independence Relations of Control Variablescited by 8
  11. 2023On Identifiability of Conditional Causal Effectscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Identifying Causal Effects Under Functional Dependenciescited by 3
  2. 2024Causal Effect Identification in a Sub-Population with Latent Variablescited by 1
  3. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0