Addressing Positivity Violations in Continuous Interventions through Data-Adaptive Strategies

Positivity violations pose a key challenge in the estimation of causal effects, particularly for continuous interventions. Current approaches for addressing this issue include the use of weights or modified treatment policies. While effective in many contexts, these methods can result in estimands that do not always align well with the original research question, thereby compromising interpretability. In this paper, we introduce a novel diagnostic tool-the non-overlap ratio-to detect positivity violations. To address these violations while maintaining interpretability, we propose a data-adaptive solution, specifically the "most feasible" intervention strategy. Our strategy operates on a unit-specific basis. For a given intervention of interest, we first assess whether the intervention value is feasible for each unit. For units with sufficient support-conditional on confounders-we adhere to the intervention of interest. However, for units lacking sufficient support, we do not assign the actual intervention value of interest. Instead, we assign the closest feasible value within the support region. The non-overlap ratio provides a diagnostic summary of such support across the population. We propose an estimator using g-computation coupled with flexible conditional density estimation to identify high- and low-support regions and to estimate this new estimand. Through simulations, we demonstrate that our method effectively reduces bias across various scenarios by addressing positivity violations. Moreover, when positivity violations are absent, the method successfully recovers the standard estimand. We further validate its practical utility using real-world data from the CHAPAS-3 trial, which enrolled HIV-positive children in Zambia and Uganda.