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

  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},
  volume={31 2},
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… 

Figures and Tables from this paper

Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

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

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

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

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

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



An alternative robust estimator of average treatment effect in causal inference

This work proposes an alternative robust approach to estimating the average treatment effect based on observational data in the challenging situation when neither a plausible parametric outcome model nor a reliable parametric propensity score model is available.

On estimating regression-based causal effects using sufficient dimension reduction

SUMMARY In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this

Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations


This work considers an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression and proposes a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions.

Covariate selection for the nonparametric estimation of an average treatment effect

Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely

Estimation of Regression Coefficients When Some Regressors are not Always Observed

Abstract In applied problems it is common to specify a model for the conditional mean of a response given a set of regressors. A subset of the regressors may be missing for some study subjects either

A Semiparametric Approach to Dimension Reduction

The semiparametric approach reveals that in the inverse regression context while keeping the estimation structure intact, the common assumption of linearity and/or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression.

Analysis of semiparametric regression models for repeated outcomes in the presence of missing data

Abstract We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a

A Distributional Approach for Causal Inference Using Propensity Scores

  • Z. Tan
  • Mathematics, Economics
  • 2006
Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin's potential outcomes framework for causal inference

Outcome‐adaptive lasso: Variable selection for causal inference

This work proposes the outcome‐adaptive lasso for selecting appropriate covariates for inclusion in propensity score models to account for confounding bias and maintaining statistical efficiency, and presents theoretical and simulation results indicating that this approach can perform variable selection in the presence of a large number of spurious covariates.