Categories → Causal Wizard Concept , Process , Causal Inference , Study Design , Method
A statistical estimate is an approximation of an unknown value based on a sample of data.
A statistical estimate is the output of a method used to calculate a value that is unknown or uncertain based on a sample of the data. It is a calculated value that represents an approximation of the true value of a population parameter, such as the mean or variance. The goal of statistical estimation is to obtain a reliable and accurate estimate that is close to the true value of the population parameter.
In the context of causal inference, statistical estimation is used to estimate the causal effect of an intervention, treatment or exposure. For example, a researcher might want to know whether a new drug is effective in reducing symptoms of a particular disease. They might conduct a randomized controlled trial, where some participants receive the drug and others receive a placebo. By comparing the outcomes between the two groups, the researcher can estimate the causal effect of the drug. Estimation is also used in observational studies.
Statistical estimation is essential in causal inference because it helps researchers to quantify the uncertainty associated with their estimates. This uncertainty can arise due to random sampling variability or other sources of bias in the data. By using statistical estimation techniques, researchers can obtain an estimate of the causal effect that takes into account this uncertainty, which helps to ensure that the estimate is reliable and accurate.