Estimation and inference for high-dimensional nonparametric additive instrumental-variables regression
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Summary
A more data-driven approach by considering the nonparametric additive models between the instruments and the treatments while keeping a linear model between the treatments and the outcome so that the coefficients therein can directly bear causal interpretation.
- Type
- preprint
- Published
- 2022-03-31
- Cited by
- 2
- References
- 63
- Access
- Open access
- OpenAlex
- https://openalex.org/W4226400038
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:247922489
Keywords
Instrumental variable, Causal inference, Inference, Additive model, Estimator
References
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- High-dimensional instrumental variables regression and confidence sets
- Sparse Linear Models and L1-Regularized 2SLS with High-Dimensional Endogenous Regressors and Instruments
- Functional additive regression
- PPAR/RXR Regulation of Fatty Acid Metabolism and Fatty Acid ω-Hydroxylase (CYP4) Isozymes: Implications for Prevention of Lipotoxicity in Fatty Liver Disease
- Oracle Inequalities and Optimal Inference under Group Sparsity
- A Constrained ℓ1 Minimization Approach to Sparse Precision Matrix Estimation
- Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization
- Local asymptotics for regression splines and confidence regions
- A model-free approach for detecting interactions in genetic association studies
- Genetic and Genomic Analysis of a Fat Mass Trait with Complex Inheritance Reveals Marked Sex Specificity
- VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS
- A statistical framework for differential network analysis from microarray data
- Endogeneity in High Dimensions
- High dimensional single index models
- Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics
- Additive Regression and Other Nonparametric Models
- Confidence intervals for low dimensional parameters in high dimensional linear models
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Tuning parameter selectors for the smoothly clipped absolute deviation method.
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