Causal Inference for Statistics, Social and Biomedical Sciences: Unlocking the Secrets of Cause and Effect

Causal inference is a statistical technique that allows researchers to make inferences about the causal relationships between variables. In other words, it allows researchers to determine whether or not one variable causes another variable. Causal inference is a powerful tool that can be used to answer a wide range of questions in statistics, social sciences, and biomedical sciences.

The history of causal inference can be traced back to the early days of statistics. In the 18th century, the Scottish philosopher David Hume argued that it is impossible to prove that one event causes another event. Hume's argument was based on the fact that we can never be certain that the two events are not simply correlated with each other.

In the 19th century, the English statistician Ronald Fisher developed a method for testing causal relationships between variables. Fisher's method, known as the randomized controlled trial (RCT),is still widely used today. RCTs are designed to minimize the effects of confounding variables, which are variables that can influence both the independent and dependent variables in a study.

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

by Guido W. Imbens

4.6 out of 5

Language	:	English
File size	:	18489 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	955 pages

FREE

In the 20th century, the development of causal inference methods continued. In the 1960s, the American statistician Judea Pearl developed a graphical model of causality, which is now known as the Pearl's ladder. Pearl's ladder provides a framework for understanding the different types of causal relationships that can exist between variables.

In the 1990s, the American statistician Donald Rubin developed a method for estimating the causal effect of a treatment in observational studies. Rubin's method, known as propensity score matching, is now widely used in social sciences and biomedical sciences.

The key concepts of causal inference include:

• Causality: Causality is the relationship between a cause and an effect. A cause is an event that leads to another event. An effect is an event that is caused by another event.
• Confounding: Confounding is a variable that influences both the independent and dependent variables in a study. Confounding can make it difficult to determine whether or not the independent variable is actually causing the dependent variable.
• Randomized controlled trial: A randomized controlled trial is a type of study in which participants are randomly assigned to receive either the treatment or the control condition. RCTs are designed to minimize the effects of confounding variables.
• Observational study: An observational study is a type of study in which participants are not randomly assigned to receive the treatment or the control condition. Observational studies are often used when it is not possible to conduct an RCT.
• Propensity score matching: Propensity score matching is a statistical method for estimating the causal effect of a treatment in observational studies. Propensity score matching is used to create a group of treated participants who are similar to a group of untreated participants on all of the observed confounding variables.

Causal inference is used in a wide range of applications in statistics, social sciences, and biomedical sciences. Some of the most common applications of causal inference include:

• Evaluating the effectiveness of interventions: Causal inference can be used to evaluate the effectiveness

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

by Guido W. Imbens

4.6 out of 5

Language	:	English
File size	:	18489 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	955 pages

FREE