Collaborative causal inference on distributed data. (arXiv:2208.07898v1 [stat.ME])

The development of technologies for causal inference with the privacy
preservation of distributed data has attracted considerable attention in recent
years. To address this issue, we propose a quasi-experiment based on data
collaboration (DC-QE) that enables causal inference from distributed data with
privacy preservation. Our method preserves the privacy of private data by
sharing only dimensionality-reduced intermediate representations, which are
individually constructed by each party. Moreover, our method can reduce both
random errors and biases, whereas existing methods can only reduce random
errors in the estimation of treatment effects. Through numerical experiments on
both artificial and real-world data, we confirmed that our method can lead to
better estimation results than individual analyses. With the spread of our
method, intermediate representations can be published as open data to help
researchers find causalities and accumulated as a knowledge base.



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