1 item has been added to your cart.
Causal Inference: The Mixtape |
||||||||||||||||||||||||||||||||
 Click to enlarge |
As an Amazon Associate, StataCorp earns a small referral credit from
qualifying purchases made from affiliate links on our site.
VitalSource eBook details
VitalSource eBooks are read using the Bookshelf How to access your eBook1) Visit Bookshelf online to sign in or create an account.
2) Once logged in, click redeem in the upper right corner. Enter your eBook code. Your eBook code will be in your order confirmation email under the eBook's title.
3) The eBook will be added to your library. You may then download Bookshelf on other devices and sync your library to view the eBook.
Bookshelf is available on the following:
Bookshelf allows you to have 2 computers and 2 mobile devices activated at any given time.
I was amazed at the VitalSource way of presenting the books....
Everything looks perfectly typeset, but yet you can "flip"
through the book in the same way you would "flip" through a very
long web page in your web browser. And best of all, whenever I
have my tablet with me, my books are just a swipe away.
— Michael Mitchell
Senior statistician at the USC Children's Data Network, author of four Stata Press books, and former UCLA statistical consultant who envisioned and designed the UCLA Statistical Consulting Resources website. Return policy for eBooksStata Press eBooks are nonreturnable and nonrefundable. ×eBook not available for this title |
|
||||||||||||||||||||||||||||||
Comment from the Stata technical groupCausal Inference: The Mixtape is a book for practitioners. The purpose of the book is to allow researchers to understand causal inference and work with their data to answer relevant questions in the area. It is the emphasis on the use of statistical software that sets Cunningham's book apart. In each chapter, theoretical details are clearly presented, followed by how to apply the theory to answer causal inference problems using statistical software. The examples are accompanied by readily available data and replication code. The book starts with some basic concepts and then moves into the most commonly used causal inference models and estimators. Topics include directed acyclical graphs, potential outcome models, matching and subclassification, regression discontinuity, instrumental variables, difference in differences, and synthetic control. The inclusion and discussion of synthetic control and directed acyclical graphs differentiates this book from others in the literature, which do not cover these topics or do so tangentially. The structure of the book lends itself to teaching a course on causal inference, but at the same time it is a useful reference for any researcher delving into causal inference. |
||||||||||||||||||||||||||||||||
Table of contentsView table of contents >> |
||||||||||||||||||||||||||||||||
 |
 |
 |
 |
 |
 |
)