Propensity Score Analysis With Fallible Covariates
A Note on a Latent Variable Modeling Approach
Bibliographic Data
| ID | 20283180 |
|---|---|
| Authors | Tenko Raykov (0000-0002-8911-5116, Michigan State University, East Lansing, MI, USA, corresponding author) |
| Year | 2012 |
| Volume | 72 |
| Issue | 5 |
| Pages | 715-733 |
| Publication date | 2012-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164412440999 |
| OpenAlex | W2077977962 |
| Language | EN |
| Citations received | 5 |
| References cited | 15 |
A latent variable modeling approach that permits estimation of propensity scores in observational studies containing fallible independent variables is outlined, with subsequent examination of treatment effect. When at least one covariate is measured with error, it is indicated that the conventional propensity score need not possess the desirable property of bias adjustment with respect to possible prior group differences. For this setting, a modified propensity score is discussed that is based on true scores on fallible covariates and perfectly measured covariates, if available. This modified score can be recommended to use for average treatment effect evaluation in circumstances where selection into groups occurs on corresponding underlying latent dimensions measured with error. The proposed propensity score procedure is illustrated with an example
Covariate · Econometrics · Latent variable · Observational study · Propensity score matching · Statistics · Advanced Causal Inference Techniques · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials
Improving Causal Inference
How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study
The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability
Unreliable Continuous Treatment Indicators in Propensity Score Analysis
The EffectLiteR Approach for Analyzing Average and Conditional Effects
Statistical Theories of Mental Test Scores
Introduction to Psychometric Theory
Observational Studies
Beyond SEM
Average causal effects from nonrandomized studies
Statistical Analysis of Sets of Congeneric Tests
Using Propensity Scores to Help Design Observational Studies
For objective causal inference, design trumps analysis
A Review and Synthesis of the Measurement Invariance Literature
The central role of the propensity score in observational studies for causal effects
Estimating causal effects of treatments in randomized and nonrandomized studies.
An Extension of the Propensity Score Adjustment Method for the Analysis of Group Differences in MIMIC Models
| Unique citing works | 5 |
|---|---|
| Citations per year | 0.45 |
| Citation span | 2015 - 2024 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |