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  Private Causal Inference using Propensity Scores

Lee, S., Gresele, L., Park, M., & Muandet, K. (submitted). Private Causal Inference using Propensity Scores.

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https://arxiv.org/abs/1905.12592 (Any fulltext)
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 Creators:
Lee, SK, Author
Gresele, L1, 2, Author              
Park, M, Author
Muandet, K, Author
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The use of propensity score methods to reduce selection bias when determining causal effects is common practice for observational studies. Although such studies in econometrics, social science, and medicine often rely on sensitive data, there has been no prior work on privatising the propensity scores used to ascertain causal effects from observed data. In this paper, we demonstrate how to privatise the propensity score and quantify how the added noise for privatisation affects the propensity score as well as subsequent causal inference. We test our methods on both simulated and real-world datasets. The results are consistent with our theoretical findings that the privatisation preserves the validity of subsequent causal analysis with high probability. More importantly, our results empirically demonstrate that the proposed solutions are practical for moderately-sized datasets.

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 Dates: 2019-05
 Publication Status: Submitted
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