2010
AISTATS
AISTATS 2010
Sufficient covariates and linear propensity analysis
Abstract
Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.
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Interdisciplinary Bridge
- Artificial Intelligence and Knowledge & Reasoning
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Trend Setter
- Causal Inference
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Keyword Pioneer
- observational data
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Hot Topic Early Bird
- treatment effect