# Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data

@article{Tan2018ModelassistedIF, title={Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data}, author={Z. Tan}, journal={arXiv: Statistics Theory}, year={2018} }

Consider the problem of estimating average treatment effects when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity score models. The conventional approach of model building and fitting iteratively can be difficult to implement, depending on ad hoc choices of what variables are included. In addition, uncertainty from the iterative process of model selection is complicated and often ignored in subsequent inference about treatmentâ€¦Â CONTINUE READING

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