Cheng Zhang – Causal Decision Making | ML in PL 22
Causal Decision Making by Cheng Zhang (Microsoft Research)
Causal inference is essential for data-driven decision-making across domains such as business engagement, medical treatment, or policymaking. Building a framework that can answer real-world causal questions at scale is critical. However, research on deep learning, causal discovery, and inference has evolved separately. In this talk, I will present a 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). Moreover, I will talk about how such a framework can be used with different real-world data, including time series or considering latent confounders. In the end, I will cover different application scenarios with the Microsoft causal AI suite. We hope that our work bridges the causality and deep learning communities leading to real-world impact.
Cheng Zhang is a Principal Researcher, leading causal AI for decision making at Microsoft Research Cambridge (MSRC), UK. She is an expert in deep generative models, causal discovery, causal inference, and decision-making under uncertainty. She has published in all top venues in machine learning, including NeurIPS, ICML, AIStats, UAI, and AAAI. Apart from research expertise, she is also experienced in enabling real-world impact in different domains.
The talk was delivered during ML in PL Conference 2022 organized by a non-profit NGO called ML in PL Association.
ML in PL Association website: https://mlinpl.org/
ML in PL Conference 2022 website: https://conference2022.mlinpl.org/
ML In PL Conference 2023 website: https://conference2023.mlinpl.org/
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ML in PL Association was founded based on the experiences in organizing of the ML in PL Conference (formerly PL in ML), the ML in PL Association is a non-profit organization devoted to fostering the machine learning community in Poland and Europe and promoting a deep understanding of ML methods. Even though ML in PL is based in Poland, it seeks to provide opportunities for international cooperation.
