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Zijian Guo

Software

RobustIV: Robust Instrumental Variable Methods in Linear Models

  • Descrioption: 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)).

  • Paper reference

Guo, Z., Kang, H., Cai, T. T. and Small, D. S. (2018).
Confidence Intervals for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting.
Journal of the Royal Statistical Society: Series B, 80(4), 793-815.

Guo, Z. (2021).
Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling.
To appear in Journal of the Royal Statistical Society: Series B

Guo, Z., Kang, H., Cai, T. T. and Small, D. S. (2018).
Testing Endogeneity with High Dimensional Covariates.
The Journal of Econometrics, 207(1), 175-187.

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)).

  • Paper reference

* Cai, T, Cai, T. T. and Guo, Z. (2021).
Optimal Statistical Inference for Individualized Treatment Effects in High-dimensional Models.
Journal of the Royal Statistical Society: Series B, 2021, 83(4): 669-719.

Guo, Z., Rakshit, P., Herman, D., and Chen, J. (2021).
Inference for Case Probability in High-dimensional Logistic Regression.
Journal of Machine Learning Research, 22(254), 1-54

Guo, Z., Renaux, C., Bühlmann, P., and Cai, T. T. (2021).
Group Inference in High Dimensions with Applications to Hierarchical Testing.
Electronic Journal of Statistics, 15(2), 6633-6676.

Ma, R.,Guo, Z., Cai, T. T., and Li, H. (2020).
Statistical Inference for Genetic Relatedness Based On High-Dimensional Logistic Regression.
To appear in Statistica Sinica.

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 is here, R package available at https:cran.r-project.org/web/packages/TSCI/index.html.

  • Papre reference ,

Guo, Z. and Bühlmann, P. (2022).
Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables
Technical Report

Control functionIV: 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 is here, R package available at https:cran.r-project.org/web/packages/controlfunctionIV/index.html .

  • Paper reference

Guo, Z. and Small, D. S. (2016).
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models.
Journal of Machine Learning Research, 17(100):1-35, 2016.

Li, S. and Guo, Z. (2020).
Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables.
Technical Report

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.

  • Paper reference

Guo, Z. (2020).
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies .
Minor Revision at Journal of the American Statistical Association

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.

  • Paper reference

Guo, Z., Yuan W. and Zhang, C. (2019).
Local Inference in Additive Models with Decorrelated Local Linear Estimator.
Technical Report

DDL: Doubly Debiased Lasso

  • Description: The goal of DDL is to implement the Doubly Debiased Lasso estimator proposed in <arXiv:2004.03758>. It Computes the Doubly Debiased Lasso estimator of a single regression coefficient in the high-dimensional linear model with hidden confounders and also constructs the confidence interval forthe target regression coefficient.

  • Github Repository is here, R package available at https:cran.r-project.org/web/packages/DDL/index.html.

  • Paper reference

Guo, Z. , Cevid, D., and Bühlmann, P. (2021+).
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding.
Annals of Statistics, 50 (3), 1320 - 1347.

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

  • Description: Performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177 / 0962280216686131>. Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111 / rssc.12233>. This software was made possible by Grant R03DE028410 from the The National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

  • Paper reference

Cheng J., Cheng, N. F., Guo, Z., Gregorich, S., Ismail, A. I. and Gansky, S. A. (2018).
Mediation Analysis for Count and Zero-Inflated Count Data.
Statistical Methods in Medical Research, 27(9), 2756-2774.

Guo, Z., Small, D. S., Gansky, S. A., and Cheng, J. (2018).
Mediation Analysis for Count and Zero-Inflated Count Data without Sequential Ignorability.
Journal of the Royal Statistical Society: Series C, 67(2), 371-394.