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We often want to reduce or eliminate the influence of some Confounding factor when designing an experiment. We can sometimes do this by "blocking", which involves the separate consideration of blocks of data that have different levels of exposure to that factor.
The regression uses as independent variables not only the one or ones whose effects on the dependent variable are being studied, but also any potential confounding variables, thus avoiding omitted variable bias. "Confounding variables" in this context means other factors that not only influence the dependent variable (the outcome) but also ...
The stronger the confounding of treatment and covariates, and hence the stronger the bias in the analysis of the naive treatment effect, the better the covariates predict whether a unit is treated or not. By having units with similar propensity scores in both treatment and control, such confounding is reduced.
In epidemiology, one type is "confounding by indication", [19] which relates to confounding from observational studies. Because prognostic factors may influence treatment decisions (and bias estimates of treatment effects), controlling for known prognostic factors may reduce this problem, but it is always possible that a forgotten or unknown ...
Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their work. Understanding the source of statistical bias can help to assess whether the observed results are close to actuality. Issues of statistical bias has been argued to be closely linked to issues of statistical validity. [1]
Overmatching, or post-treatment bias, is matching for an apparent mediator that actually is a result of the exposure. [12] If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced. [13] Overmatching thus causes statistical bias. [13]
These two controls, when both are successful, are usually sufficient to eliminate most potential confounding variables: it means that the experiment produces a negative result when a negative result is expected, and a positive result when a positive result is expected. Other controls include vehicle controls, sham controls and comparative controls.
An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator. Bias is a distinct concept from consistency: consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased (see bias versus consistency for more).