Causal Sufficient Dimension Reduction for Multiple Continuous Exposures with an Application to Environmental Mixtures
多重连续暴露的因果充分降维及其环境混合物应用
Estimating causal effects with multivariate continuous exposures is challenging because causal exposure-response surfaces can be high-dimensional, complicating estimation and interpretation of joint exposure effects. Such settings arise in environmental epidemiology, where interest centers on the health effects of chemical and pollutant mixtures. We develop causal sufficient dimension reduction (CSDR), a semiparametric framework for representing causal exposure-response…