AAAI 2019
Counterfactual Reasoning in Observational Studies
Abstract
To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures).
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Context
- Venue
- AAAI Conference on Artificial Intelligence
- Archive span
- 1980-2026
- Indexed papers
- 28718
- Paper id
- 441431715047342240