How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes

This article presents a unified framework for efficiently estimating causal treatment effects on exponential‑family outcomes, extending target regularization beyond Gaussian assumptions, deriving bias analysis for plug‑in estimators, proposing DR and TMLE‑based estimators, and validating them on synthetic and real datasets.

Neural Networkscausal inferenceexponential family

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How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes

AntTech

Dec 13, 2019 · Artificial Intelligence

Ant Group Showcases AI Research at NeurIPS 2019: Deep Exponential Family and Graph Neural Network Innovations

At NeurIPS 2019 in Vancouver, Ant Group presented several award‑winning papers—including a deep exponential family distribution estimation method and a conditional graph logic network for retrosynthesis—while also demonstrating AI‑driven applications such as smart waste classification, underscoring its broad AI research and deployment efforts.

NeurIPSRetrosynthesisexponential family

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Ant Group Showcases AI Research at NeurIPS 2019: Deep Exponential Family and Graph Neural Network Innovations