Deep End-to-end Causal Inference

Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

Keywords: end-to-end causal inference, causal discovery, causal inference, causal machine learning

TL;DR: We combine causal discovery and causal inference in a single deep learning framework, allowing users to proceed from data to estimation of causal quantities.

Abstract: 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 causal inference, preventing straightforward combination of methods from both fields. In this work, we develop Deep End-to-end Causal Inference (DECI), a non-linear additive noise model with neural network functional relationships 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 asymptotically recover the ground truth causal graph and treatment effects when correctly specified. 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2202.02195/code)