From healthcare data to decisions: The target trial framework

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Many important decisions about human health must be made in the absence of evidence from randomized trials, which are often impractical or too lengthy to provide a timely answer. In these cases, healthcare data provide a way to generate evidence to inform decision-making. Causal inference from these observational data can be conceptualized as an attempt to emulate a hypothetical randomized trial – the target trial – that would have answered the causal question of interest. Through practical applications, this talk describes how the target trial framework can help to reduce bias in the effect estimates derived from observational analyses and provide timely evidence for health decision-making. It also examines the critical role of expert knowledge, which raises questions about the potential use of AI tools to support causal inference research in the health sciences.

This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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* Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay
Start date
26 March 2024 at 16:00 CET Geneva | 11:00-12:30 EDT, New York | 23:00-00:30 CST, Beijing
End date
26 March 2024 at 17:30 CET Geneva | 11:00-12:30 EDT, New York | 23:00-00:30 CST, Beijing
Duration
90 minutes (including 30 minutes networking)
Programme stream
- Discovery - AI and Health
Topics
- Health
UN SDGs
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From healthcare data to decisions: The target trial framework