---
product_id: 449665644
title: "Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction"
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---

# Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

**Price:** 1761 kr
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- **What is this?** Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
- **How much does it cost?** 1761 kr with free shipping
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## Description

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Review: Ok - Ok
Review: An importante book for understand causal inference - I bought this book for assess experiments in social science field. It is well written and contains all the information required in order to have a good preparation in causal inference. I appreciated the logical path and his linear structure!

## Features

- Cambridge University Press

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Customer Reviews | 4.8 out of 5 stars 90 Reviews |

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## Customer Reviews

### ⭐⭐⭐⭐⭐ Ok
*by C***R on 24 June 2026*

Ok

### ⭐⭐⭐⭐⭐ An importante book for understand causal inference
*by L***A on 21 March 2020*

I bought this book for assess experiments in social science field. It is well written and contains all the information required in order to have a good preparation in causal inference. I appreciated the logical path and his linear structure!

### ⭐⭐⭐⭐⭐ A good reference text/introduction to causal inference using Propensity Score analysis
*by A***S on 22 January 2017*

Some people are of the opinion that statistics should replace calculus in high school/college curricula because of the importance of analytics in the modern business world. I think a similar argument could be made about replacing some of the more esoteric parts of graduate statistics/econometrics with causal inference theory. If that opinion is correct, this would be an excellent reference text. Causal inference theory is important because the regression techniques now taught to young social scientists as methods of determining cause and effect assume endogeneity when the data often don't support such an assumption. They also impose a linear model on the data that can be similarly inappropriate. The non-parametric techniques discussed by Rubin and Imbens, while having their own assumptions, are applicable to a wider range of problems. Rubin and Imbens summarize the voluminous literature on propensity score and related causal inference techniques in a manner that is accessible to someone with a solid background in statistics (both frequentist and Bayesian). I read the book cover to cover and, despite already knowing something about Propensity Score techniques, learned a great deal. They begin with randomized experiments then explain how the mathematical models developed for such methods are also applicable to observational studies. They then discuss various methods of using the Propensity Score along with tests of the plausibility of such models and bias limits when some of the assumptions in these models are relaxed. One complaint I have is that the different types of exact matching are barely discussed. Considering the growing importance of techniques like Coarsened Exact Matching, this seems like a significant oversight. In addition, the book contains no exercises making it difficult to use as a textbook without some supplementary material. All in all, though, this work is a must have for those engaged in Causal Inference either academically or in the business world. Even those not making active use of these techniques might find applications to their empirical work once they understand how to properly use Propensity Score analysis.

## Frequently Bought Together

- Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
- Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)

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*Last updated: 2026-10-31*