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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Matching,s 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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Regression Discontinuity Design,t so different as the slight difference in their score could be due to random error or temporary factors like fatigue or fever during the exam. However, in many universities, a student with a score of 89 would receive a B plus, while a score of 90 would secure an A. This one-point difference resulti

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Difference-in-Differences,anking crisis. As the ripple effects of the risk spread across the world, banking stocks plummeted, instilling fears in the financial system. Haunted by the aftermath of the 2008 financial crisis, where about 500 banks collapsed, President Biden reassured American investors and depositors within hou

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