Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization

Keisuke Hirano; Guido W. Imbens

doi:10.1023/A:1020371312283

Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization

Keisuke Hirano, Guido W. Imbens

Research output: Contribution to journal › Article › peer-review

730 Scopus citations

Abstract

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 catheterization (RHC) studied in Connors et al (1996), and we find that our estimator gives stable estimates over a wide range of values for the two parameters governing the selection of variables.

Original language	English (US)
Pages (from-to)	259-278
Number of pages	20
Journal	Health Services and Outcomes Research Methodology
Volume	2
Issue number	3-4
DOIs	https://doi.org/10.1023/A:1020371312283
State	Published - 2001
Externally published	Yes

Keywords

Casual inference
Propensity score
Right heart catheterization
Treatment effects
Variable selection

ASJC Scopus subject areas

Health Policy
Public Health, Environmental and Occupational Health

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10.1023/A:1020371312283

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@article{bdda848f8de94c46a3eb6a38eb5fa8e2,

title = "Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization",

abstract = "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 catheterization (RHC) studied in Connors et al (1996), and we find that our estimator gives stable estimates over a wide range of values for the two parameters governing the selection of variables.",

keywords = "Casual inference, Propensity score, Right heart catheterization, Treatment effects, Variable selection",

author = "Keisuke Hirano and Imbens, {Guido W.}",

note = "Funding Information: We are grateful to the SUPPORT study for making their data available, to Enrico Moretti for help working with the data, and to Donald Rubin, Rajeev Dehejia, and three anonymous referees for comments. Sejin Min, Marcos Rangel and Yue Xu provided excellent research assistance. Financial support for this research was generously provided through NSF grants SES-9985257 (Hirano) and SBR-9818644 and SES-0136789 (Imbens).",

year = "2001",

doi = "10.1023/A:1020371312283",

language = "English (US)",

volume = "2",

pages = "259--278",

journal = "Health Services and Outcomes Research Methodology",

issn = "1387-3741",

publisher = "Springer Netherlands",

number = "3-4",

}

TY - JOUR

T1 - Estimation of causal effects using propensity score weighting

T2 - An application to data on right heart catheterization

AU - Hirano, Keisuke

AU - Imbens, Guido W.

N1 - Funding Information: We are grateful to the SUPPORT study for making their data available, to Enrico Moretti for help working with the data, and to Donald Rubin, Rajeev Dehejia, and three anonymous referees for comments. Sejin Min, Marcos Rangel and Yue Xu provided excellent research assistance. Financial support for this research was generously provided through NSF grants SES-9985257 (Hirano) and SBR-9818644 and SES-0136789 (Imbens).

PY - 2001

Y1 - 2001

N2 - 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 catheterization (RHC) studied in Connors et al (1996), and we find that our estimator gives stable estimates over a wide range of values for the two parameters governing the selection of variables.

AB - 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 catheterization (RHC) studied in Connors et al (1996), and we find that our estimator gives stable estimates over a wide range of values for the two parameters governing the selection of variables.

KW - Casual inference

KW - Propensity score

KW - Right heart catheterization

KW - Treatment effects

KW - Variable selection

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DO - 10.1023/A:1020371312283

M3 - Article

AN - SCOPUS:0035761721

SN - 1387-3741

VL - 2

SP - 259

EP - 278

JO - Health Services and Outcomes Research Methodology

JF - Health Services and Outcomes Research Methodology

IS - 3-4

ER -

Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization

Abstract

Keywords

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