Meaning
Statistical technique that adjusts for missing data or selection bias by weighting observations by the inverse of their inclusion probability. Applying inverse probability weighting helps researchers estimate the true effect of a treatment or a marketing campaign in a non-randomized population. It gives more weight to individuals who are under-represented in the data set.
This correction is essential for drawing valid conclusions from observational market data.
Bias Correction
Data collected from real world transactions often suffers from inherent imbalances that skew results. The weighting mechanism rebalances the sample to resemble a controlled experiment. This allows for a more accurate assessment of contract performance or customer behaviour.
Inference Validity
Commercial strategy relies on the ability to predict how a change in pricing or distribution will affect different segments. Without this adjustment, a company might over-invest in a strategy that only works for a specific sub-group. It provides a mathematically sound basis for broader market entry decisions.
Weighting Mechanism
Calculating the weights requires a separate model to predict the probability of each observation occurring. These probabilities are then inverted and applied to the primary analysis. This two step process ensures that the final results are not biased by the original sampling method.
It remains a standard procedure for causal inference.
