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Demystifying Causal Inference : Public Policy Applications with R

Dayal, Vikram; Murugesan, Anand - 2023

John Snow and causal inference -- RStudio and R -- Regression and simulation -- Potential outcomes -- Causal graphs … intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal …

Persistent link: https://www.econbiz.de/10014375030

Non-random exposure to exogenous shocks : theory and applications

Borusyak, Kirill; Hull, Peter - 2020

Persistent link: https://www.econbiz.de/10012306198

How to control for many covariates? : reliable estimators based on the propensity score

Huber, Martin; Lechner, Michael; Wunsch, Conny - 2010

and other variants of matching, as well as different parametric models. The simulation design used is based on real data …

Persistent link: https://www.econbiz.de/10009154559

How to control for many covariates? : reliable estimators based on the propensity score

Huber, Martin; Lechner, Michael; Wunsch, Conny - 2010

and other variants of matching, as well as different parametric models. The simulation design used is based on real data …

Persistent link: https://www.econbiz.de/10009240317

Predictive inference under model misspecification with an application to assessing the marginal predictive content of money for output

Armah, Nii Ayi (contributor); Swanson, Norman R. (contributor) - 2006

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap...