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Activity Number:
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321
- Nonparametric Inference Under Shape Constraints
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract #322836
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Title:
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Nonparametric Doubly Robust Testing for Continuous Treatment Effects via Smoothness and via Shape Constraints
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Author(s):
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Charles Doss* and Guangwei Weng and Lan Wang
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Companies:
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University of Minnesota and University of Minnesota and University of Miami
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Keywords:
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causal inference;
observational data;
doubly robust;
nonparametric;
concavity
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Abstract:
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A large majority of literature on evaluating the significance of a treatment effect based on observational data has been focused on discrete treatments. These methods are not applicable to drawing inference for a continuous treatment, which arises in many important applications. To adjust for confounders when evaluating a continuous treatment, existing inference methods often rely on discretizing the treatment or using (possibly misspecified) parametric models for the effect curve. Completely nonparametric doubly robust methods for inference in this setting are not yet available. We develop global and local doubly robust procedures for making inference on the continuous treatment effect curve. We develop procedures based just on smoothness-type assumptions, and procedures based on the shape constraint of concavity. We illustrate the new methods via simulations and a study of a constructed dataset relating the effect of nurse staffing hours on hospital performance.
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Authors who are presenting talks have a * after their name.
