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10.1515/jci-2014-0022 http://scihub22266oqcxt.onion/10.1515/jci-2014-0022 C4749263!4749263!26877909     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Causal+Inference 2015 ; 3 (1): 25-40 Nephropedia Template TP {{tp|p=26877909|t=2015. A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments |pdf=|usr=}}{{26877909}} gab.com Text A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments JO: J Causal Inference http://www.kidney.de/pget.php?&tw=1&pmid=26877909 #FOAvip In this article, we study the causal inference problem with a continuous treatment variable using propensity score-based methods. For a continuous treatment, the generalized propensity score is defined as the conditional density of the treatment-level given covariates (confounders). The dose?response function is then estimated by inverse probability weighting, where the weights are calculated from the estimated propensity scores. When the dimension of the covariates is large, the traditional nonparametric density estimation suffers from the curse of dimensionality. Some researchers have suggested a two-step estimation procedure by first modeling the mean function. In this study, we suggest a boosting algorithm to estimate the mean function of the treatment given covariates. In boosting, an important tuning parameter is the number of trees to be generated, which essentially determines the trade-off between bias and variance of the causal estimator. We propose a criterion called average absolute correlation coefficient (AACC) to determine the optimal number of trees. Simulation results show that the proposed approach performs better than a simple linear approximation or L2 boosting. The proposed methodology is also illustrated through the Early Dieting in Girls study, which examines the influence of mothers? overall weight concern on daughters? dieting behavior. Twit Text FOAVip A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments ????J Causal Inference http://www.kidney.de/pget.php?&tw=1&pmid=26877909 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26877909 #FOAvip English Wikipedia <ref>{{Cite pmid|26877909}}</ref> A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments #MMPMID26877909 Zhu Y; Coffman DL; Ghosh D J Causal Inference 2015[Mar]; 3 (1): 25-40 PMID26877909show ga In this article, we study the causal inference problem with a continuous treatment variable using propensity score-based methods. For a continuous treatment, the generalized propensity score is defined as the conditional density of the treatment-level given covariates (confounders). The dose?response function is then estimated by inverse probability weighting, where the weights are calculated from the estimated propensity scores. When the dimension of the covariates is large, the traditional nonparametric density estimation suffers from the curse of dimensionality. Some researchers have suggested a two-step estimation procedure by first modeling the mean function. In this study, we suggest a boosting algorithm to estimate the mean function of the treatment given covariates. In boosting, an important tuning parameter is the number of trees to be generated, which essentially determines the trade-off between bias and variance of the causal estimator. We propose a criterion called average absolute correlation coefficient (AACC) to determine the optimal number of trees. Simulation results show that the proposed approach performs better than a simple linear approximation or L2 boosting. The proposed methodology is also illustrated through the Early Dieting in Girls study, which examines the influence of mothers? overall weight concern on daughters? dieting behavior. äA Boosting Algorithm for Estimating Generalized Propensity Scores with(epmc).pdf DeepDyve Pubget Overpricing