  Causal inference is essential for data-driven decision making across domains
such as business engagement, medical treatment and policy making. However,
research on causal discovery has evolved separately from inference methods,
preventing straight-forward combination of methods from both fields. In this
work, we develop Deep End-to-end Causal Inference (DECI), a single flow-based
non-linear additive noise model that takes in observational data and can
perform both causal discovery and inference, including conditional average
treatment effect (CATE) estimation. We provide a theoretical guarantee that
DECI can recover the ground truth causal graph under standard causal discovery
assumptions. Motivated by application impact, we extend this model to
heterogeneous, mixed-type data with missing values, allowing for both
continuous and discrete treatment decisions. Our results show the competitive
performance of DECI when compared to relevant baselines for both causal
discovery and (C)ATE estimation in over a thousand experiments on both
synthetic datasets and causal machine learning benchmarks across data-types and
levels of missingness.
