RobustIV: Robust Instrumental Variable Methods in Linear Models

• Description: Inference for the treatment effect with possibly invalid instrumental variables via TSHT ('Guo et al.’ (2018)) and SearchingSampling ('Guo’ (2021)), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.’ (2018)).
  

• GitHub repository: here, R package: RobustIV on CRAN.
  

• Paper reference
  

SIHR: Statistical Inference in High Dimensional Regression

• Description: Inference procedures in the high-dimensional setting for (1) linear functionals in generalized linear regression ('Cai et al.’ (2019), 'Guo et al.’ (2020) , 'Cai et al.’ (2021)), (2) individual treatment effects in generalized linear regression, (3) quadratic functionals in generalized linear regression ('Guo et al.’ (2019)).

• GitHub repository: here, R package: SIHR on CRAN.

• Paper reference
  

TSCI: Tools for Causal Inference with Possibly Invalid Instrumental Variables

• Description: Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) “Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables” .

• GitHub repository: here, R package: TSCI on CRAN.

• Paper reference

controlfunctionIV: Control Function Methods with Possibly Invalid Instrumental Variables

• Description: Inference with control function methods for nonlinear outcome models when the model is known ('Guo and Small’ (2016)) and when unknown but semiparametric ('Li and Guo’ (2020)), using the Control Function method and the SpotIV method.

• GitHub repository: here, R package: controlfunctionIV on CRAN .

• Paper reference

MaximinInfer

• Description: MaximinInfer is a package that implements the sampling and aggregation method for the covariate shift maximin effect, which was proposed in Guo (2020). It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.

• GitHub repository: here, R package: MaximinInfer on CRAN.

• Paper reference

DLL: Decorrelated Local Linear Estimator

• Description: Implementation of the Decorrelated Local Linear estimator proposed in <arXiv:1907.12732>. It constructs the confidence interval for the derivative of the function of interest under the high-dimensional sparse additive model.

• GitHub repository: here, R package: DLL on CRAN

• Paper reference

DDL: Doubly Debiased Lasso

• Description: DDL implements the Doubly Debiased Lasso estimator proposed in the Annals of Statistics article. It computes the estimator and a confidence interval for a target regression coefficient in a high-dimensional linear model with hidden confounders.

• GitHub repository: here, R package: DDL on CRAN.

• Paper reference

maczic: Mediation Analysis for Count and Zero-Inflated Count Data

• Description: Performs causal mediation analysis for count and zero-inflated count data with or without a post-treatment confounder; calculates power for mediation, direct, and total effects; and supports sensitivity analysis. The methods are described by Cheng et al. (2018) <doi:10.1177/0962280216686131> and Guo et al. (2018) <doi:10.1111/rssc.12233>. This software was supported by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

• R package: maczic on CRAN.

• Paper reference