This chapter provides an overview of the econometric and statistical methods for drawing inference on causal effects from randomized experiments under the potential outcome approach. Well-designed and conducted randomized experiments are generally considered to be the gold standard for obtaining objective causal inferences, but the design and analysis of experiments require to address a number of statistical issues. The chapter first discusses design and inferential issues in classical randomized experiments, by also providing insights on the relative advantages and drawbacks of alternative types of classical randomized experiments. Then, it discusses complications arising from clustered randomization, multiple site experiments, re-randomization as well as issues arising in the design and analysis of randomized experiments with post-treatment complications, sequential and dynamic experiments, and experiments in settings with interference. Recently developed approaches for estimating causal effects using machine learning methods are also described. The chapter concludes with some discussion on the external and internal validity of randomized experiments.
Design and analysis of experiments / Alessandra Mattei, Fabrizia Mealli, Anahita Nodehi. - STAMPA. - (2021), pp. 1-44.
Design and analysis of experiments
Alessandra Mattei;Fabrizia Mealli;Anahita Nodehi
2021
Abstract
This chapter provides an overview of the econometric and statistical methods for drawing inference on causal effects from randomized experiments under the potential outcome approach. Well-designed and conducted randomized experiments are generally considered to be the gold standard for obtaining objective causal inferences, but the design and analysis of experiments require to address a number of statistical issues. The chapter first discusses design and inferential issues in classical randomized experiments, by also providing insights on the relative advantages and drawbacks of alternative types of classical randomized experiments. Then, it discusses complications arising from clustered randomization, multiple site experiments, re-randomization as well as issues arising in the design and analysis of randomized experiments with post-treatment complications, sequential and dynamic experiments, and experiments in settings with interference. Recently developed approaches for estimating causal effects using machine learning methods are also described. The chapter concludes with some discussion on the external and internal validity of randomized experiments.File | Dimensione | Formato | |
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