### Abstract

We examine the practicality of propensity score methods for estimating causal treatment effects conditional on intermediate posttreatment outcomes (principal effects) in the context of randomized experiments. In particular, we focus on the sensitivity of principal causal effect estimates to violation of principal ignorability, which is the primary assumption that underlies the use of propensity score methods to estimate principal effects. Under principal ignorability (PI), principal strata membership is conditionally independent of the potential outcome under control given the pre-treatment covariates; i.e. there are no differences in the potential outcomes under control across principal strata given the observed pretreatment covariates. Under this assumption, principal scores modeling principal strata membership can be estimated based solely on the observed covariates and used to predict strata membership and estimate principal effects. While this assumption underlies the use of propensity scores in this setting, sensitivity to violations of it has not been studied rigorously. In this paper, we explicitly define PI using the outcome model (although we do not actually use this outcome model in estimating principal scores) and systematically examine how deviations from the assumption affect estimates, including how the strength of association between principal stratum membership and covariates modifies the performance. We find that when PI is violated, very strong covariate predictors of stratum membership are needed to yield accurate estimates of principal effects.

Original language | English (US) |
---|---|

Pages (from-to) | 2857-2875 |

Number of pages | 19 |

Journal | Statistics in Medicine |

Volume | 28 |

Issue number | 23 |

DOIs | |

State | Published - Oct 15 2009 |

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### Keywords

- Intermediate outcomes
- Principal ignorability
- Principal scores
- Principal stratification
- Propensity scores
- Randomized experiments

### ASJC Scopus subject areas

- Epidemiology
- Statistics and Probability

### Cite this

*Statistics in Medicine*,

*28*(23), 2857-2875. https://doi.org/10.1002/sim.3669

**On the use of propensity scores in principal causal effect estimation.** / Jo, Booil; Stuart, Elizabeth.

Research output: Contribution to journal › Article

*Statistics in Medicine*, vol. 28, no. 23, pp. 2857-2875. https://doi.org/10.1002/sim.3669

}

TY - JOUR

T1 - On the use of propensity scores in principal causal effect estimation

AU - Jo, Booil

AU - Stuart, Elizabeth

PY - 2009/10/15

Y1 - 2009/10/15

N2 - We examine the practicality of propensity score methods for estimating causal treatment effects conditional on intermediate posttreatment outcomes (principal effects) in the context of randomized experiments. In particular, we focus on the sensitivity of principal causal effect estimates to violation of principal ignorability, which is the primary assumption that underlies the use of propensity score methods to estimate principal effects. Under principal ignorability (PI), principal strata membership is conditionally independent of the potential outcome under control given the pre-treatment covariates; i.e. there are no differences in the potential outcomes under control across principal strata given the observed pretreatment covariates. Under this assumption, principal scores modeling principal strata membership can be estimated based solely on the observed covariates and used to predict strata membership and estimate principal effects. While this assumption underlies the use of propensity scores in this setting, sensitivity to violations of it has not been studied rigorously. In this paper, we explicitly define PI using the outcome model (although we do not actually use this outcome model in estimating principal scores) and systematically examine how deviations from the assumption affect estimates, including how the strength of association between principal stratum membership and covariates modifies the performance. We find that when PI is violated, very strong covariate predictors of stratum membership are needed to yield accurate estimates of principal effects.

AB - We examine the practicality of propensity score methods for estimating causal treatment effects conditional on intermediate posttreatment outcomes (principal effects) in the context of randomized experiments. In particular, we focus on the sensitivity of principal causal effect estimates to violation of principal ignorability, which is the primary assumption that underlies the use of propensity score methods to estimate principal effects. Under principal ignorability (PI), principal strata membership is conditionally independent of the potential outcome under control given the pre-treatment covariates; i.e. there are no differences in the potential outcomes under control across principal strata given the observed pretreatment covariates. Under this assumption, principal scores modeling principal strata membership can be estimated based solely on the observed covariates and used to predict strata membership and estimate principal effects. While this assumption underlies the use of propensity scores in this setting, sensitivity to violations of it has not been studied rigorously. In this paper, we explicitly define PI using the outcome model (although we do not actually use this outcome model in estimating principal scores) and systematically examine how deviations from the assumption affect estimates, including how the strength of association between principal stratum membership and covariates modifies the performance. We find that when PI is violated, very strong covariate predictors of stratum membership are needed to yield accurate estimates of principal effects.

KW - Intermediate outcomes

KW - Principal ignorability

KW - Principal scores

KW - Principal stratification

KW - Propensity scores

KW - Randomized experiments

UR - http://www.scopus.com/inward/record.url?scp=70449397203&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449397203&partnerID=8YFLogxK

U2 - 10.1002/sim.3669

DO - 10.1002/sim.3669

M3 - Article

C2 - 19610131

AN - SCOPUS:70449397203

VL - 28

SP - 2857

EP - 2875

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 23

ER -