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Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference

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  • Peter Z. Schochet

Abstract

This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the finite-population model) or randomly selected from a vaguely defined universe (the super-population model). Both approaches allow for heterogeneity of treatment effects. Appropriate estimation methods and asymptotic moments are discussed for each model using simple differences-in-means estimators and those that include baseline covariates. An empirical application using a large-scale education RCT shows that the choice of the finite- or super-population approach can matter. Thus, the choice of framework and sensitivity analyses should be specified and justified in the analysis protocols.

Suggested Citation

  • Peter Z. Schochet, 2013. "Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 219-238, June.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:3:p:219-238
    DOI: 10.3102/1076998611432176
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    References listed on IDEAS

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    Cited by:

    1. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    2. Tim Kautz & Peter Z. Schochet & Charles Tilley, "undated". "Comparing Impact Findings from Design-Based and Model-Based Methods: An Empirical Investigation," Mathematica Policy Research Reports b7656ddce20f4007b71836e99, Mathematica Policy Research.
    3. repec:mpr:mprres:8128 is not listed on IDEAS
    4. Peter Z. Schochet, "undated". "What is Design-Based Causal Inference and Why Should I Use It?," Mathematica Policy Research Reports 82a207630f374ef6a7dfd4a60, Mathematica Policy Research.

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