Causal Inference in R

Metadata

• Author: Malcolm Barrett
• Full Title: Causal Inference in R
• URL: https://news.dataelixir.com/t/t-l-ehjdte-jtuylyjttl-m/

Highlights

• Welcome to Causal Inference in R. Answering causal questions is critical for scientific and business purposes, but techniques like randomized clinical trials and A/B testing are not always practical or successful. The tools in this book will allow readers to better make causal inferences with observational data with the R programming language. (View Highlight)
• By its end, we hope to help you:
  1. Ask better causal questions.
  2. Understand the assumptions needed for causal inference
  3. Identify the target population for which you want to make inferences
  4. Fit causal models and check their problems
  5. Conduct sensitivity analyses where the techniques we use might be imperfect (View Highlight)
• There are also several other excellent books on causal inference. This book is different in its focus on R, but it’s still helpful to see this area from other perspectives. A few books you might like: • Causal Inference: What If? • Causal Inference: The Mixtape • The Effect (View Highlight)
• The first book is focused on epidemiology. The latter two are focused on econometrics. We also recommend The Book of Why Pearl and Mackenzie (2018) for more on causal diagrams. (View Highlight)