Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
@article{Hirano2000EfficientEO, title={Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score}, author={Keisuke Hirano and Guido Imbens and Geert Ridder}, journal={Econometrics eJournal}, year={2000} }
We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the pre-treatment variables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pre-treatment, the…
2,329 Citations
The Value of Knowing the Propensity Score for Estimating Average Treatment Effects
- Economics, MathematicsSSRN Electronic Journal
- 2016
In a treatment effect model with unconfoundedness, treatment assignments are not only independent of potential outcomes given the covariates, but also given the propensity score alone. Despite this…
Propensity score specification for optimal estimation of average treatment effect with binary response
- EconomicsStatistical methods in medical research
- 2020
This paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect, and is compared against two recently introduced methods.
Efficient propensity score regression estimators of multivalued treatment effects for the treated
- Economics, MathematicsJournal of Econometrics
- 2018
We study the role of the propensity scores in estimating treatment effects for the treated with a multi-valued treatment. Assume assignment to one of the multiple treatments is random given observed…
A Propensity Score Adjustment for Multiple Group Structural Equation Modeling
- Mathematics
- 2006
AbstractIn the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences…
An alternative robust estimator of average treatment effect in causal inference
- Economics, MathematicsBiometrics
- 2018
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.
Improved Estimation of Average Treatment Effects on the Treated: Local Efficiency, Double Robustness, and Beyond
- Mathematics, Economics
- 2018
Estimation of average treatment effects on the treated (ATT) is an important topic of causal inference in econometrics and statistics. This problem seems to be often treated as a simple modification…
Propensity Score Modeling and Evaluation
- Economics
- 2016
In causal inference for binary treatments, the propensity score is defined as the probability of receiving the treatment given covariates. Under the ignorability assumption, causal treatment effects…
Correlation and efficiency of propensity score-based estimators for average causal effects
- Mathematics, EconomicsCommun. Stat. Simul. Comput.
- 2017
This study investigates how the efficiency of matching, inverse probability weighting, and doubly robust estimators change under the case of correlated covariates and shows that the covariate correlations may increase or decrease the variances of the estimators.
Propensity score weighting analysis and treatment effect discovery
- Mathematics, EconomicsStatistical methods in medical research
- 2018
This work develops analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness.
EMPIRICAL BALANCING WEIGHTS AND GLOBAL EFFICIENT ESTIMATION OF AVERAGE TREATMENT EFFECT
- Mathematics, Economics
- 2012
There have been important recent developments of globally efficient estimation of average treatment effect based on observational data, where the assignment of a binary treatment variable is…
References
SHOWING 1-10 OF 82 REFERENCES
The central role of the propensity score in observational studies for causal effects
- Economics
- 1983
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.…
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…
Characterizing the effect of matching using linear propensity score methods with normal distributions
- Economics
- 1992
SUMMARY Matched sampling is a standard technique for controlling bias in observational studies due to specific covariates. Since Rosenbaum & Rubin (1983), multivariate matching methods based on…
When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects
- Mathematics
- 1999
The problem of how to control for covariates is endemic in evaluation research. Covariate-matching provides an appealing control strategy, but with continuous or high-dimensional covariate vectors,…
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
- Mathematics, Economics
- 1984
Abstract The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that…
Estimating exposure effects by modelling the expectation of exposure conditional on confounders.
- EconomicsBiometrics
- 1992
In order to estimate the causal effects of one or more exposures or treatments on an outcome of interest, one has to account for the effect of "confounding factors" which both covary with the…
Simple and Bias-Corrected Matching Estimators for Average Treatment Effects
- Mathematics, Economics
- 2002
In Abadie and Imbens (2006), it was shown that simple nearest-neighbor matching estimators include a conditional bias term that converges to zero at a rate that may be slower than N1/2. As a result,…
Matching using estimated propensity scores: relating theory to practice.
- MathematicsBiometrics
- 1996
These results delineate the wide range of settings in which matching on estimated linear propensity scores performs well, thereby providing useful information for the design of matching studies and applying theoretical approximations to practice.
Semiparametric regression estimation in the presence of dependent censoring
- Mathematics
- 1995
SUMMARY We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured…
Matching As An Econometric Evaluation Estimator
- Economics
- 1998
This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more…