Sufficient dimension reduction for feasible and robust estimation of average causal effect

Trinetri Ghosh, Yanyuan Ma, Xavier De Luna

Research output: Contribution to journalArticlepeer-review

Abstract

To estimate the treatment effect in an observational study, we use a semi-parametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrink-age estimator that combines the two. The proposed estimators retains the double robustness property, while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight.

Original languageEnglish (US)
Pages (from-to)821-842
Number of pages22
JournalStatistica Sinica
Volume31
Issue number2
DOIs
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Sufficient dimension reduction for feasible and robust estimation of average causal effect'. Together they form a unique fingerprint.

Cite this