Titlebook: Demystifying Causal Inference; Public Policy Applic Vikram Dayal,Anand Murugesan Textbook 2023 The Editor(s) (if applicable) and The Author

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Potential Outcomes,n statistical inference, and the Neyman-Rubin causal model guides us in thinking about causal estimands and in estimating causal effects. In this chapter, we provide an intuitive introduction to the Neyman-Rubin causal model.

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Christian Heath,Gillian Nichollscal leadership positions make a difference in policy outcomes, or whether smaller classes lead to better student learning outcomes? The answer often would be the experimental method. Researchers use this method to investigate cause-and-effect relationships by randomly assigning units, such as indivi

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Alain Trognon,Corinne Grusenmeyers processing observational data to create a comparison group where treated and control units are similar on observed characteristics. Advocates of matching argue that creating observably similar groups allows for an apples-to-apples comparison, which can be used to estimate the causal effect of a tr

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https://doi.org/10.1007/978-3-319-93623-9n rapidly in recent years. This literature uses diverse approaches, and is published in technical form in journals. In Sect.?., we provide a simplified non-technical overview. In Sect.?., we provide R code for (1) simulation to get a feel for the concepts and (2) estimation with real data. We begin