Deep End-to-end Causal Inference
This work addresses the challenge of integrating causal methods for data-driven decision-making in domains like business and medicine, though it appears incremental as it builds on existing causal discovery and inference techniques.
The authors tackled the problem of combining causal discovery and inference into a single model, developing Deep End-to-end Causal Inference (DECI) that handles heterogeneous, mixed-type data with missing values and achieves competitive performance in over a thousand experiments on synthetic and benchmark datasets.
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.