DiPPS
Differentially Private Propensity Scores for Bias Correction
In surveys, it is typically up to the individuals to decide if they want to participate or not, which leads to participation bias: the individuals willing to share their data might not be representative of the entire population. Similarly, there are cases where one does not have direct access to any data of the target population and has to resort to publicly available proxy data sampled from a different distribution. In this paper, we present Differentially Private Propensity Scores for Bias Correction (DiPPS), a method for approximating the true data distribution of interest in both of the above settings
inactive
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entered showcase: 2023-03-16
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entry updated: 2024-04-16
This project has not yet been evaluated by the C4DT Factory team.
We will be happy to evaluate it upon request.
Experiments
Python
other