Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
arXiv:2605.21548v1 Announce Type: cross Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency - where observed variables share no latent confounders - or the pretreatment assumption, which limits covariates to those unaffected by the treatment or outcome. These requirements are often unrealistic in practice, and global learning becomes computationally prohibitive in high-dimensional settings.To address
The proliferation of complex AI models and the increasing demand for robust causal inference in various applied fields necessitate more flexible and less restrictive methodological advancements.
This research addresses fundamental limitations in current causal inference methods, offering pathways to more accurate and reliable AI decision-making where traditional assumptions are often unfulfilled.
The ability to perform unbiased causal effect estimation without strict pretreatment or causal sufficiency assumptions reduces the barriers to applying AI in complex, real-world systems.
- · AI researchers and developers
- · Healthcare and social sciences
- · Policy makers
- · Data scientists
- · Organizations relying on overly simplistic causal models
- · Traditional statistical methodologies
Improved reliability and broader applicability of AI systems in domains with complex, unobservable causal structures.
Faster development and deployment of AI agents in scenarios where complete data on confounders or treatment timing is impractical to obtain.
Enhanced trust in AI-driven insights for critical decision-making, potentially accelerating AI adoption in sensitive sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG