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因果中介分析：选择与渐近有效推断AI 解读

Abstract Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In…

利用Lasso进行效应修饰的选择后推断AI 解读

Abstract Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to…

稳健 Q 学习AI 解读

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic…

不定时间跨度下动态治疗方案的构建AI 解读

Existing methods for estimating optimal dynamic treatment regimes are limited to cases where a utility function is optimized over a fixed time period. We develop an estimation procedure for the optimal dynamic treatment regime over an indefinite time period and derive associated large-sample results. The proposed method can be used to estimate the optimal dynamic treatment regime in chronic disease settings. We illustrate this by simulating a dataset corresponding to a…