Related papers: Federated Estimation of Causal Effects from Observational Data

Federated Estimation of Causal Effects from Observational Data

URL: http://arxiv.org/abs/2106.00456v1
Date: Mon, 31 May 2021 08:06:00 GMT
Title: Federated Estimation of Causal Effects from Observational Data
Authors: Thanh Vinh Vo, Trong Nghia Hoang, Young Lee, Tze-Yun Leong
Abstract summary: We present a novel framework for causal inference with federated data sources. We assess and integrate local causal effects from different private data sources without centralizing them.
Score: 19.657789891394504
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed. Till date, most insight into the causal inference requires data to be stored in a central repository. We present a novel framework for causal inference with federated data sources. We assess and integrate local causal effects from different private data sources without centralizing them. Then, the treatment effects on subjects from observational data using a non-parametric reformulation of the classical potential outcomes framework is estimated. We model the potential outcomes as a random function distributed by Gaussian processes, whose defining parameters can be efficiently learned from multiple data sources, respecting privacy constraints. We demonstrate the promise and efficiency of the proposed approach through a set of simulated and real-world benchmark examples.

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