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• Deep End-to-End Causal Inference

Deep End-to-End Causal Inference

Abstract

Causal inference is essential for data-driven decision-making across domains such as business engagement, medical treatment or policy-making. However, research on causal discovery and inference has evolved separately, and the combination of the two domains is nontrivial. In this talk, I will present our Deep End-to-end Causal Inference (DECI) framework, a single flow-based method that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect estimation (CATE). We provide a theoretical guarantee that DECI can recover the ground truth under mild assumptions. In addition, our method can handle heterogeneous, real-world, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Moreover, the design principle of our method can generalize beyond DECI, providing a general End-to-end Causal Inference (ECI) recipe, which enables different ECI frameworks to be built using existing methods. Our results show the superior performance of DECI when compared to relevant baselines for both causal discovery and (C)ATE estimation on over a thousand experiments, with both synthetic datasets and various other causal machine learning benchmark datasets. We hope that our work bridges the causal discovery and inference communities.

Bio

Cheng Zhang is a Principal Researcher at the Machine intelligence group at Microsoft Research Cambridge (MSRC), UK. Currently, she leads the project Azua on efficient decision making in MSRC. She is interested in both machine learning theory, including Bayesian deep learning, approximate inference and causality for efficient decision making, as well as various machine learning applications with business and social impact.