arXiv:2605.14284v2 Announce Type: replace Abstract: Comparative evaluation of multiple dynamic treatment policies is essential for healthcare and policy decisions, yet conventional longitudinal causal inference methods estimate each in isolation, preventing information sharing across counterfactuals. We demonstrate that this separate estimation paradigm induces a structurally uncontrolled second-order bias, inflating finite-sample variance even after standard debiasing with longitudinal targeted maximum likelihood estimation(LTMLE). To address this, we propose a policy-aware reparameterization
This research addresses a fundamental methodological challenge in causal inference for complex dynamic systems, a critical 'picking a winner' or 'avoiding a loser' capability for systems of all scopes, which is becoming more acute as AI-driven policy decisions become more prevalent in critical sectors.
Improving the accuracy and reliability of causal effect estimation in longitudinal settings has direct implications for the efficacy of AI-driven policy making in healthcare, social policy, and economic interventions, enhancing the trustworthiness and impact of such systems.
The proposed 'policy-aware reparameterization' offers a more robust method for comparing multiple dynamic treatment policies, potentially leading to better-informed and more stable policy decisions in complex environments.
- · Healthcare policy makers
- · Social science researchers
- · AI ethicists and safety researchers
- · Data scientists in policy roles
- · Organizations relying on isolated causal inference methods
- · Policy initiatives based on flawed comparative evaluations
More accurate comparative evaluation of dynamic policies in healthcare and social interventions.
Reduced risk and improved outcomes in areas where AI-driven policy is deployed, due to better foundational causal analysis.
Increased adoption of sophisticated causal inference methodologies in critical decision-making systems, potentially influencing trust in autonomous AI agents for policy generation.
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Read at arXiv cs.LG