# Doubly robust estimation of average treatment effect revisited

@article{Guo2020DoublyRE, title={Doubly robust estimation of average treatment effect revisited}, author={Keli Guo and Chuyun Ye and Jun Fan and Li-Zhi Fang Division of Applied Mathematics and Hong Kong Baptist University and Hong Kong and Center for Statistics and Data Science and Beijing Normal University and Zhuhai and China. and School of Statistics and Beijing}, journal={arXiv: Statistics Theory}, year={2020} }

The research described herewith is to re-visit the classical doubly robust estimation of average treatment effect by conducting a systematic study on the comparisons, in the sense of asymptotic efficiency, among all possible combinations of the estimated propensity score and outcome regression. To this end, we consider all nine combinations under, respectively, parametric, nonparametric and semiparametric structures. The comparisons provide useful information on when and how to efficiently…

## References

SHOWING 1-10 OF 22 REFERENCES

### Estimating Conditional Average Treatment Effects

- Mathematics, Economics
- 2014

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness…

### Estimation of Regression Coefficients When Some Regressors are not Always Observed

- Mathematics
- 1994

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…

### Doubly robust estimation of the causal effects in the causal inference with missing outcome data

- MathematicsJournal of Ambient Intelligence and Humanized Computing
- 2018

Simulation studies show that the doubly robust estimator of the causal effect which was constructed by the authors has better statistical properties in terms of bias and mean squared error (MSE); the values calculated from the asymptotic variance of the proposed estimator and the empirical variance are relatively close.

### Doubly robust estimation of causal effects.

- Economics, MathematicsAmerican journal of epidemiology
- 2011

The authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method.

### On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

- Mathematics, Economics
- 1998

The role of propensity score in the efficient estimation of the average treatment effects is examined. If the treatment is ignorable given some observed characteristics, it is shown that the…

### Rejoinder: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

- Mathematics
- 2008

When outcomes are missing for reasons beyond an investigator's control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to…

### Semiparametric double robust and efficient estimation for mean functionals with response missing at random

- MathematicsComput. Stat. Data Anal.
- 2018

### Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions

- Mathematics, EconomicsSSRN Electronic Journal
- 2013

We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step, but retain a fully nonparametric specification in the first…

### Assignment to Treatment Group on the Basis of a Covariate

- Mathematics
- 1976

When assignment to treatment group is made solely on the basis of the value of a covariate, X, effort should be concentrated on estimating the conditional expectations of the dependent variable Y…

### Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

- PsychologyStatistics in medicine
- 2004

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score,…