Causal Inference in R

Causal Inference in R is a comprehensive guide that introduces you to the methods and practices of determining causality in data through the lens of R programming. By navigating its pages and examples, you'll master the application of causal models and statistical approaches to real-world problems, enabling more informed data-driven decisions.

What this Book will help me do

• Understand the principles and foundations of causal inference to identify causality in data.
• Apply methods like propensity score matching and instrumental variables using R.
• Leverage real-world case studies to analyze and resolve confounding factors and make better data claims.
• Harness statistical methods and R tools to address real-world data challenges innovatively.
• Develop a strategy for integrating causal models into decision-making workflows with confidence.

Author(s)

Subhajit Das, the author of Causal Inference in R, is an accomplished applied scientist with over a decade of experience in causal inference methodologies and data analysis. Subhajit is passionate about empowering learners by breaking down complex concepts into manageable, clear explanations. His expertise ensures that readers not only understand the theory behind causal inference but are also able to apply it effectively using R.

Who is it for?

This book is ideal for data analysts, statisticians, and researchers looking to deepen their understanding of causal inference techniques using R. Whether you're a practitioner aiming to enhance your data-driven decision-making skills or a student aspiring to tackle advanced causal analysis, this book provides pathbreaking insights. It's suitable for individuals at beginner to intermediate skill levels in data analysis, especially those in public policy, economics, and the social sciences who utilize R regularly.