@article{1481480,
      recid = {1481480},
      author = {Dayal, Vikram, and Murugesan, Anand,},
      title = {Demystifying causal inference : public policy applications  with R /},
      pages = {1 online resource (386 pages) :},
      abstract = {This book provides an accessible introduction to causal  inference and data analysis with R, specifically for a  public policy audience. It aims to demystify these topics  by presenting them through practical policy examples from a  range of disciplines. It provides a hands-on approach to  working with data in R using the popular tidyverse package.  High quality R packages for specic causal inference  techniques like ggdag, Matching, rdrobust, dosearch etc.  are used in the book. The book is in two parts. The first  part begins with a detailed narrative about John Snows  heroic investigations into the cause of cholera. The  chapters that follow cover basic elements of R, regression,  and an introduction to causality using the potential  outcomes framework and causal graphs. The second part  covers specific causal inference methods, including  experiments, matching, panel data,  difference-in-differences, regression discontinuity design,  instrumental variables and meta-analysis, with the help of  empirical case studies of policy issues. The book adopts a  layered approach that makes it accessible and intuitive,  using helpful concepts, applications, simulation, and data  graphs. Many public policy questions are inherently causal,  such as the effect of a policy on a particular outcome.  Hence, the book would not only be of interest to students  in public policy and executive education, but also to  anyone interested in analysing data for application to  public policy.},
      url = {http://library.usi.edu/record/1481480},
      doi = {https://doi.org/10.1007/978-981-99-3905-3},
}