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Activity Number:
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295
- Causal, Robust, and Machine Learning for Survival Outcomes
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #317004
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Title:
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Towards Double Robustness under the Cox Marginal Structural Model
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Author(s):
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Ronghui Xu* and Denise Rava and Jelena Bradic
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Companies:
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University of California at San Diego and Ucsd and University of California, San Diego
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Keywords:
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inverse probability weighting;
augmented IPW;
machine learning;
causal inference
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Abstract:
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The Cox Marginal Structural Model (MSM) has been widely used to draw causal inference from survival data of observational studies. The typical estimation approach under the MSM is inverse probability weighting (IPW), which is known to be inefficient and also inconsistent when the IP weights are not correctly estimated. Due to the non-collapsibility of the Cox regression model, double robustness (DR) is not a straightforward and possibly an ill-posed problem. We explore the extent to which, both theoretically and empirically, the augmented IPW (AIPW) may achieve DR.
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Authors who are presenting talks have a * after their name.
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