Causal inference
Causal Inference
Causal inference is a process used in statistics and epidemiology that allows for the determination of cause-and-effect relationships between variables.
Pronunciation
Causal: /ˈkɔːzəl/ Inference: /ˈɪnfərəns/
Etymology
The term "causal" is derived from the Latin word "causa" which means "cause". "Inference" is derived from the Latin word "inferre" which means "to bring in".
Definition
Causal inference is a method used to determine whether a change in one variable can result in a change in another variable. This is done by establishing a causal relationship between the two variables, rather than just a correlation.
Related Terms
- Correlation: A statistical measure that describes the association between random variables.
- Causality: The relationship between cause and effect.
- Confounding: A distortion of the association between an exposure and an outcome that occurs when the study group is not representative of the population.
- Counterfactual: The concept of what would have happened to the same individuals under a different scenario.
Methodology
Causal inference involves a number of steps and methodologies, including randomized controlled trials, observational studies, and longitudinal studies. These methods help to establish a causal relationship between variables and to rule out the possibility of confounding variables.
Applications
Causal inference is widely used in various fields such as medicine, economics, social sciences, and public health. It helps in understanding the cause and effect relationship between variables, which is crucial in making predictions and formulating policies.
See Also
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