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Activity Number:
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165
- SLDS CSpeed 2
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #318356
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Title:
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Causal Mediation Analysis Based on Partial Linear Models
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Author(s):
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Xizhen Cai* and Yeying Zhu and Yuan Huang
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Companies:
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Williams College and University of Waterloo and Yale University
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Keywords:
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mediation effect;
high-dimensional data;
partial linear model
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Abstract:
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In this talk, we propose a set of partial linear regression models to estimate the direct and indirect effects of mediation analysis. We allow a nonlinear relationship among the baseline covariates and the response variables in each model. Since we are only interested in estimating the coefficients for the treatment and the mediator in the structural models, we assume partial linear models where the baseline covariates are regarded as nuisance. Our estimates can be interpreted as causal effects without the linearity assumption. We also propose variable selection procedures when the set of mediators is high-dimensional. Simulation results show the superior performance of our proposed method and a data application is conducted when the set of candidate mediators are high-dimensional methylations.
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Authors who are presenting talks have a * after their name.
