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Latent variables and propensity score matching: a simulation study with application to data from the Programme for International Student Assessment in Poland

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  • Maciej Jakubowski
Abstract
This paper examines how including latent variables can benefit propensity score matching. Latent variables can be estimated from the observed manifest variables and used in matching. This paper demonstrates the benefits of such an approach by comparing it with a method where the manifest variables are directly used in matching. Estimating the propensity score on the manifest variables introduces a measurement error that can be limited with estimating the propensity score on the estimated latent variable. We use Monte Carlo simulations to test how the proposed approach behaves under distinct circumstances found in practice, and then apply it to real data. Using the estimated latent variable in the propensity score matching limits the measurement error bias of the treatment effects’ estimates and increases their precision. The benefits are larger for small samples and with better information about the latent variable available. Copyright The Author(s) 2015

Suggested Citation

  • Maciej Jakubowski, 2015. "Latent variables and propensity score matching: a simulation study with application to data from the Programme for International Student Assessment in Poland," Empirical Economics, Springer, vol. 48(3), pages 1287-1325, May.
    • Handle: RePEc:spr:empeco:v:48:y:2015:i:3:p:1287-1325
    DOI: 10.1007/s00181-014-0814-x
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    Cited by:

    1. Trang Quynh Nguyen & Elizabeth A. Stuart, 2020. "Propensity Score Analysis With Latent Covariates: Measurement Error Bias Correction Using the Covariate’s Posterior Mean, aka the Inclusive Factor Score," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 598-636, October.
    2. José M. Cordero & Víctor Cristóbal & Daniel Santín, 2018. "Causal Inference On Education Policies: A Survey Of Empirical Studies Using Pisa, Timss And Pirls," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 878-915, July.
    3. Jerrim, John & Lopez-Agudo, Luis Alejandro & Marcenaro-Gutierrez, Oscar D. & Shure, Nikki, 2017. "What happens when econometrics and psychometrics collide? An example using the PISA data," Economics of Education Review, Elsevier, vol. 61(C), pages 51-58.
    4. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.

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    More about this item

    Keywords

    Program evaluation; Matching; Treatment effects; Measurement error; Human capital; PISA; Tracking; C14; C15; C21;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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