Categories → Causal Inference , Study Design , Method
Inference in statistics is the process of using data to draw conclusions about a population. Causal inference aims to identify causal relationship between a treatment or intervention and an outcome.
Inference in statistics is the process of using data to make conclusions about a population. Inference involves making generalizations based on sample data and estimating the uncertainty associated with those generalizations. In machine learning, inference means the same thing, but more specifically, this implies using a statistically-optimized model to generate output, such as predictions.
Causal inference is a specific type of inference that aims to identify the causal relationship between an intervention or treatment and an outcome of interest. In causal inference, the goal is to determine whether a treatment or intervention caused a change in the outcome, while accounting for other factors that may have influenced the outcome.
Causal inference involves identifying potential confounding variables and using statistical methods to control for their effects. Researchers may also use randomized controlled trials or natural experiments to establish causal relationships between treatments and outcomes.
It is important to note that causal inference is subject to various sources of bias and uncertainty, and researchers must carefully consider the quality of the data and the appropriateness of the statistical methods used to ensure the validity and reliability of their conclusions.