Patarakin: Новая страница: «{{Book |Description=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. By its end, we hope to help you: # Ask better causal questions. # Understand the assumptions n...»

2025-06-28T08:54:04Z

Новая страница: «{{Book |Description=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. By its end, we hope to help you: # Ask better causal questions. # Understand the assumptions n...»

Новая страница

{{Book
|Description=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. By its end, we hope to help you:
# Ask better causal questions.
# Understand the assumptions needed for causal inference
# Identify the target population for which you want to make inferences
# Fit causal models and check their problems
# Conduct sensitivity analyses where the techniques we use might be imperfect
|Field_of_knowledge=Информатика, Социология, Экономика, Статистика
|launch year=2025
|Website=https://www.r-causal.org/
|Inventor=Barrett
|Environment=R
}}
We use a lot of [[dplyr]] and [[ggplot2]] in this book, but we won’t explain their basic grammar. To learn more about starting with the [[tidyverse]], we recommend [[R for Data Science]].
You’re familiar with basic statistical modeling in R. For instance, we’ll fit many models with lm() and glm(), but we won’t discuss how they work. If you want to learn more about R’s powerful modeling functions, we recommend reading “A Review of R Modeling Fundamentals” in [[Tidy Modeling with R]].

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