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Deep End-to-end Causal Inference
Tomas Geffner · Javier Antorán · Adam Foster · Wenbo Gong · Chao Ma · Emre Kiciman · Amit Sharma · Angus Lamb · Martin Kukla · Nick Pawlowski · Miltiadis Allamanis · Cheng Zhang
Event URL: https://openreview.net/forum?id=6DPVXzjnbDK »

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 straight-forward 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.

Author Information

Tomas Geffner (University of Massachusetts, Amherst)
Javier Antorán (University of Cambridge)
Adam Foster (Microsoft)
Wenbo Gong (Microsoft)
Chao Ma (University of Cambridge)
Emre Kiciman (Microsoft Research)
Amit Sharma (Microsoft Research)
Angus Lamb (Microsoft Research)
Martin Kukla (University of Cambridge)
Nick Pawlowski (Microsoft Research)
Miltiadis Allamanis (Microsoft Research)
Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

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