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What is Design-Based Causal Inference and Why Should I Use It?

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  • Peter Z. Schochet

Abstract

This brief aims to broaden knowledge of design-based methods by describing their key concepts and how they compare to model-based methods.

Suggested Citation

  • Peter Z. Schochet, "undated". "What is Design-Based Causal Inference and Why Should I Use It?," Mathematica Policy Research Reports 82a207630f374ef6a7dfd4a60, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:82a207630f374ef6a7dfd4a6050d80c8
    as

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    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2017/causal-inference-rct.pdf
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    References listed on IDEAS

    as
    1. 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.
    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. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Randomized controlled trials; impact estimation; quantitative methods; causal inference; education interventions;
    All these keywords.

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