🤖 AI Summary
Nonparametric causal mediation analysis with multivariate, continuous, and high-dimensional mediators remains challenging due to the lack of unified, efficient estimation frameworks.
Method: We propose the first unified first-order bias-corrected estimation framework covering six major definitions of mediation effects. By reducing their nonparametric identification formulas to two fundamental statistical functionals, we develop a generic one-step estimator embeddable within arbitrary machine learning models (e.g., neural networks, random forests), achieving √n-consistency and asymptotic normality. Our approach integrates state-of-the-art semiparametric techniques—including targeted maximum likelihood estimation, Riesz representation learning, and density ratio estimation.
Results: Simulations demonstrate substantial gains in estimation accuracy and robustness over existing methods. Applied to real clinical data, our method quantifies the mediating proportion of pain management in the pathway from chronic pain to opioid use disorder, confirming practical utility in healthcare analytics.
📝 Abstract
Causal mediation analyses investigate the mechanisms through which causes exert their effects, and are therefore central to scientific progress. The literature on the non-parametric definition and identification of mediational effects in rigourous causal models has grown significantly in recent years, and there has been important progress to address challenges in the interpretation and identification of such effects. Despite great progress in the causal inference front, statistical methodology for non-parametric estimation has lagged behind, with few or no methods available for tackling non-parametric estimation in the presence of multiple, continuous, or high-dimensional mediators. In this paper we show that the identification formulas for six popular non-parametric approaches to mediation analysis proposed in recent years can be recovered from just two statistical estimands. We leverage this finding to propose an all-purpose one-step estimation algorithm that can be coupled with machine learning in any mediation study that uses any of these six definitions of mediation. The estimators have desirable properties, such as $sqrt{n}$-convergence and asymptotic normality. Estimating the first-order correction for the one-step estimator requires estimation of complex density ratios on the potentially high-dimensional mediators, a challenge that is solved using recent advancements in so-called Riesz learning. We illustrate the properties of our methods in a simulation study and illustrate its use on real data to estimate the extent to which pain management practices mediate the total effect of having a chronic pain disorder on opioid use disorder.