# Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect.

@article{Ghosh2018SufficientDR, title={Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect.}, author={Trinetri Ghosh and Yanyuan Ma and Xavier de Luna}, journal={Statistica Sinica}, year={2018}, volume={31 2}, pages={ 821-842 } }

When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and non-treated groups. We then integrate all results through imputation, inverse probability weighting and double robust augmentation estimators. Double robust estimators are locally efficient while imputation estimators are super-efficient when the response models are…

## 5 Citations

### Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

- Computer ScienceArXiv
- 2023

This work proves that the reduced set by sufﬁcient dimension reduction (SDR) is a balance score for confounding adjustment and proposes a method to use an SDR method to obtain a reduced representation set of the original covariates and then the reduction set is used for the matching method.

### Sufficient dimension reduction for average causal effect estimation

- Computer Science, MathematicsData Mining and Knowledge Discovery
- 2022

It is proved that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation, and an algorithm is developed that employs a supervised kernel dimension reduction method to learn a lowerdimensional representation from the original covariate space.

### Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal

- Business
- 2022

In observational study, the propensity score has the central role to estimate causal eﬀects. Since the propensity score is usually unknown, estimating by appropriate procedures is an indispensable…

### Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects

- Computer ScienceAISTATS
- 2022

This work proposes an energy-based model (EBM) that learns a low-dimensional representation of the variables by employing a noise contrastive loss function, and proves that this model keeps the representations partially identifiable up to some universal constant, as well as having universal approximation capability.

### SDRcausal: an R package for causal inference based on sufficient dimension reduction

- Computer Science
- 2021

Imputation through outcome regression (IMP) and Improved Augmented Inverse Probability Weighting (AIPW) .

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