Federated Learning of Causal Effects from Incomplete Observational Data
Thanh Vinh Vo Young lee Tze-Yun Leong
Decentralized and incomplete data sources are prevalent in real-world
applications, posing a formidable challenge for causal inference. These sources
cannot be consolidated into a single entity owing to privacy constraints, and
the presence of missing values within them can potentially introduce bias to
the causal estimands. We introduce a new approach for federated causal
inference from incomplete data, enabling the estimation of causal effects from
multiple decentralized and incomplete data sources. Our approach disentangles
the loss function into multiple components, each corresponding to a specific
data source with missing values. Our approach accounts for the missing data
under the missing at random assumption, while also estimating higher-order
statistics of the causal estimands. Our method recovers the conditional
distribution of missing confounders given the observed confounders from the
decentralized data sources to identify causal effects. Our framework estimates
heterogeneous causal effects without the sharing of raw training data among
sources, which helps to mitigate privacy risks. The efficacy of our approach is
demonstrated through a collection of simulated and real-world instances,
illustrating its potential and practicality.