Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization

@article{Hirano2001EstimationOC,
  title={Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization},
  author={Keisuke Hirano and Guido Imbens},
  journal={Health Services and Outcomes Research Methodology},
  year={2001},
  volume={2},
  pages={259-278}
}
  • K. HiranoG. Imbens
  • Published 1 December 2001
  • Economics, Mathematics
  • Health Services and Outcomes Research Methodology
We consider methods for estimating causal effects of treatments when treatment assignment is unconfounded with outcomes conditional on a possibly large set of covariates. Robins and Rotnitzky (1995) suggested combining regression adjustment with weighting based on the propensity score (Rosenbaum and Rubin, 1983). We adopt this approach, allowing for a flexible specification of both the propensity score and the regression function. We apply these methods to data on the effects of right heart… 

Strati cation and weighting via the propensity score in estimation of causal treatment e ects : a comparative study

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

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

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,

Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing

Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible)

Using propensity scores to estimate effects of treatment initiation decisions: State of the science

An overview of propensity scores in the context of real‐world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options.

Estimating the variance of a propensity score matching estimator: A new look at right heart catheterisation data

This thesis consists of four papers that are related to commonly used propensity score-based estimators for average causal effects.The first paper starts with the observation that researchers often

Propensity Score Modeling and Evaluation

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

On the Specification of Propensity Scores: with an Application to the WTO-Environment Debate

Monte Carlo evidence is provided indicating the benefits of over-specifying the propensity score when using weighting estimators, as well as using normalized weights, with an application assessing the environmental effects of GATT/WTO membership.

Propensity score estimation with boosted regression for evaluating causal effects in observational studies.

Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.

2D score based estimation of heterogeneous treatment effects

In the study of causal inference, statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off

NON-PARAMETRIC INTERPRETABLE SCORE BASED ESTIMATION OF HETEROGENEOUS TREATMENT EFFECTS

A non-parametric framework for estimating the Conditional Average Treatment Effect (CATE) function, which naturally stratifies treatment effects into subgroups over a 2d grid whose axis are the propensity and prognostic scores.
...

References

SHOWING 1-10 OF 23 REFERENCES

Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions.

The central role of the propensity score in observational studies for causal effects

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.

Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

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

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

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

Marginal Structural Models versus Structural nested Models as Tools for Causal inference

This paper describes an alternative new class of causal models — the (non-nested) marginal structural models (MSMs) and describes a class of semiparametric estimators for the parameters of these new models under a sequential randomization assumption.

Characterizing the effect of matching using linear propensity score methods with normal distributions

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

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

Semiparametric regression estimation in the presence of dependent censoring

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

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

Matching using estimated propensity scores: relating theory to practice.

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.