Making Effects Manifest in Randomized Experiments

Research output: Chapter in Book/Report/Conference proceedingChapter


Experimentalists want precise estimates of treatment effects and nearly always care about how treatment effects may differ across subgroups. After data collection, concern may turn to random imbalance between treatment groups on substantively important variables. Pursuit of these three goals – enhanced precision, understanding treatment effect heterogeneity, and imbalance adjustment – requires background information about experimental units. For example, one may group similar observations on the basis of such variables and then assign treatment within those blocks. Use of covariates after data have been collected raises extra concerns and requires special justification. For example, standard regression tables only approximate the statistical inference that experimentalists desire. The standard linear model may also mislead via extrapolation. After providing some general background about how covariates may, in principle, enable pursuit of precision and statistical adjustment, this chapter presents two alternative approaches to covariance adjustment: one using modern matching techniques and another using the linear model – both use randomization as the basis for statistical inference.
Original languageEnglish (US)
Title of host publicationCambridge Handbook of Experimental Political Science
EditorsJames N. Druckman, Donald P. Green, James H. Kuklinski, Arthur Lupia
PublisherCambridge University Press
ISBN (Electronic)9780511921452
StatePublished - 2011


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