An introduction to unbiased [and doubly robust] estimators
Often, data collection cannot be completely random – e.g. in clinical trials, where it would be unethical to randomly treat people with medicine, or in online surveys, where response cannot be guaranteed. In such cases, data can be biased, so any inferences drawn or machine learning models built from this data will not generalize well to the overall population. This is where unbiased estimation can come in, in which small adjustments are effectively made to the dataset to make it more representative of a random sample....