A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments

Journal of Causal Inference

Zhu Yeying¹  Ghosh Debashis²  Coffman Donna L.³ 

[1] Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada;Department of Statistics and Public Health Sciences, The Pennsylvania State University, University Park, PA, USA;The Methodology Center, The Pennsylvania State University, University Park, PA, USA;

关键词: boosting;    distance correlation;    dose–response function;    generalized propensity scores;    high dimensional;   

DOI  :  10.1515/jci-2014-0022

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown