arXiv:2605.13430v2 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher socio-economic status than the population they are meant to represent. Recovering causal effects from such sub-population is an important problem in causal inference, as estimating average treatment effects (ATE) from selected populations can result in a severely biased estimate of the ATE from the whole population. In this paper, we investigate the identifiabili
The proliferation of large-scale observational datasets, particularly in health and social sciences, is making selection bias a critical and immediate concern for accurate causal inference in AI systems.
Improving the scientific rigor of AI and statistical models, especially in high-stakes applications like healthcare, directly impacts policy decisions and the trustworthiness of AI-driven insights.
This research provides a more robust theoretical framework for understanding and mitigating selection bias, potentially leading to more reliable and generalizable AI applications across diverse populations.
- · AI/ML researchers
- · Healthcare and biobank data scientists
- · Causal inference practitioners
- · Ethical AI advocates
- · Organizations relying on superficial AI model deployment
- · Studies with unaddressed selection bias
- · AI models lacking strong theoretical foundations
More accurate causal effect estimation in observational studies will be possible, reducing misleading conclusions.
This improved accuracy will lead to more effective and equitable interventions in fields like public health and personalized medicine.
Enhanced trust in AI's ability to interpret complex real-world data could accelerate its adoption in sensitive, regulated industries.
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Read at arXiv cs.LG