JosephKang, XiaogangSu, LeiLiu, Martha L.Daviglus. (2014) Causal inference of interaction effects with inverse propensity weighting, G-computation and tree-based standardization. Statistical Analysis and Data Mining 7 :10.1002/sam.2014.7.issue-5, 323-336 /...
significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R packageRISCAto encourage the use of g-computation in causal inference. Similar content being viewed ...
QGcomp (quantile g-computation): estimating the effects of exposure mixtures. Works for continuous, binary, and right-censored survival outcomes. Flexible, unconstrained, fast and guided by modern causal inference principles - alexpkeil1/qgcomp
As we show, this assumption allows inferences about various network causal effects including direct and spillover effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii...
To quantify the proportion of intergenerational transmission that is mediated by MHD we employ the g-computation method (Table 4) (VanderWeele, 2016; Wang and Arah, 2015). It belongs to the family of causal inference meditation analyses and has been previously used in settings that try to bette...
MA407 Algorithms and Computation (0.5) MY456 Survey Methodology (0.5) MY457 Causal Inference for Observational and Experimental Studies (0.5) SA481 Population Analysis: Methods and Models (0.5) 申请截止期:Rolling,funding deadlines:26 April 2019 申请和录取情况:2.1学位,不限专业,要有大量统计和...
MA407 Algorithms and Computation (0.5) MY456 Survey Methodology (0.5) MY457 Causal Inference for Observational and Experimental Studies (0.5) SA481 Population Analysis: Methods and Models (0.5) 申请截止期:Rolling,funding deadlines:26 April 2019 ...
QGcomp (quantile g-computation): estimating the effects of exposure mixtures. Works for continuous, binary, multinomial, and right-censored survival outcomes. Flexible, unconstrained, fast and guided by modern causal inference principles Quick start ...
A time-dependent proportion of days covered (tPDC)-based algorithm was used to transform the dispensing records into continuous data as described in an earlier study.9To limit computation time and memory usage, we only registered the mode of each 30 days, resulting in 24 time periods, with ...
Our approach leverages a Transformer architecture to capture complex, long-range dependencies in time-varying covariates while enabling g-computation, a causal inference method for estimating the effects of dynamic treatment regimes. Specifically, we use a Transformer-based encoder architecture to estimate...