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In many real-world settings, such as medicine and finance causal effect is a valuable metric for decision making. For many predictive tasks, causal mechanisms provide robust estimators while existing ML-driven predictors might be vulnerable to spurious correlations. In such settings, when data is decentralized and privacy must be preserved, federated learning plays an important role. However, causal inference in a federated learning setup is a largely unexplored research area. In this paper, we learn a proxy of the underlying structural causal model (SCM) with deep generative models from decentralized observational data sources possibly containing high-dimensional variables. Based on client preference or high dimensionality of variables, we modularize the SCM mechanisms and find the minimal subset appropriate for federated learning while having rest of the mechanisms trained on individual client’s local data. When all connected together, the proxy SCM, named as the federated deep causal generative model (FeDCM ), offers estimation of any identifiable causal effect. We perform extensive experiments to illustrate the utility and performance of our approach.