Estimating causal effects among different events is of great importance to
critical fields such as drug development. Nevertheless, the data features
associated with events may be distributed across various silos and remain
private within respective parties, impeding direct information exchange between
them. This, in turn, can result in biased estimations of local causal effects,
which rely on the characteristics of only a subset of the covariates. To tackle
this challenge, we introduce an innovative disentangle architecture designed to
facilitate the seamless cross-silo transmission of model parameters, enriched
with causal mechanisms, through a combination of shared and private branches.
Besides, we introduce global constraints into the equation to effectively
mitigate bias within the various missing domains, thereby elevating the
accuracy of our causal effect estimation. Extensive experiments conducted on
new semi-synthetic datasets show that our method outperforms state-of-the-art

Estimating causal effects is a crucial task in many fields, including drug development. However, the distribution of data across different parties and the privacy concerns associated with it often hinder direct information exchange. This leads to biased estimations of local causal effects, as they rely on a limited subset of covariates. To overcome this challenge, the authors propose an innovative disentangle architecture that enables the seamless cross-silo transmission of model parameters and causal mechanisms.

The architecture combines shared and private branches to facilitate the exchange of information across different silos. By incorporating global constraints into the equation, the authors aim to mitigate bias within the missing domains and improve the accuracy of causal effect estimation. This multi-disciplinary approach involves elements from areas such as machine learning, statistics, and data privacy.

What sets this method apart from previous approaches is its ability to leverage information from multiple sources and domains while ensuring privacy. By disentangling the architecture into shared and private branches, the authors successfully bridge the gap between siloed data and enable a more comprehensive analysis of causal effects.

To evaluate the performance of their method, the authors conducted extensive experiments on new semi-synthetic datasets. The results demonstrate that their approach outperforms state-of-the-art baselines, indicating its effectiveness in addressing the challenges associated with estimating causal effects in distributed data environments.

This research has important implications for fields that rely on accurate estimation of causal effects, such as drug development. By enabling the exchange of information across silos while preserving privacy, this approach has the potential to significantly advance research and decision-making in these critical areas.

In summary, the innovative disentangle architecture proposed in this study has the potential to revolutionize the estimation of causal effects by enabling seamless cross-silo transmission of model parameters and causal mechanisms. Its successful application in drug development and other fields could lead to more accurate estimations and improved decision-making processes.

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