Transcription of Deep End-to-end Causal Inference
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Deep End-to-end Causal Inference
Deep End-to-end Causal Inference Tomas Geffner 1 Javier Antoran 2 * Adam Foster3 * Wenbo Gong3 Chao Ma3. Emre Kiciman3 Amit Sharma3 Angus Lamb 4 Martin Kukla3. Nick Pawlowski3 Miltiadis Allamanis3 Cheng Zhang3. 1 2. University of Massachusetts Amherst University of Cambridge 3 4. Microsoft Research G-Research [ ] 20 Jun 2022. 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 Inference methods, preventing straight-forward combination of methods from both fields. In this work, we de- velop 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.
synthetic datasets and other causal machine learn-ing benchmark datasets. 1. Introduction ... (CATE), with no, or incomplete, a priori knowledge of the causal graph. Existing methods for estimating causal quantities from data, which we refer to as causal inference methods, require com-plete a priori knowledge of the causal graph. On the other
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