Software
This page includes the following R packages
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 highdimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.’ (2018)).
Guo, Z., Kang, H., Cai, T. T. and Small, D. S. (2018).
Confidence Intervals for Causal Effects with Invalid Instruments using TwoStage Hard Thresholding with Voting.
Journal of the Royal Statistical Society: Series B, 80(4), 793815.
Guo, Z. (2021).
Causal Inference with Invalid Instruments: Postselection 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), 175187.
Description: Inference procedures in the highdimensional 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)).
* Cai, T, Cai, T. T. and Guo, Z. (2021).
Optimal Statistical Inference for Individualized Treatment Effects in Highdimensional Models.
Journal of the Royal Statistical Society: Series B, 2021, 83(4): 669719.
Guo, Z., Rakshit, P., Herman, D., and Chen, J. (2021).
Inference for Case Probability in Highdimensional Logistic Regression.
Journal of Machine Learning Research, 22(254), 154
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), 66336676.

Ma, R.,Guo, Z., Cai, T. T., and Li, H. (2020).
Statistical Inference for Genetic Relatedness Based On HighDimensional Logistic Regression.
To appear in Statistica Sinica.
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.rproject.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
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.rproject.org/web/packages/controlfunctionIV/index.html .
Guo, Z. and Small, D. S. (2016).
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models.
Journal of Machine Learning Research, 17(100):135, 2016.
Li, S. and Guo, Z. (2020).
Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables.
Technical Report

Guo, Z. (2020).
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies .
Minor Revision at Journal of the American Statistical Association
Guo, Z., Yuan W. and Zhang, C. (2019).
Local Inference in Additive Models with Decorrelated Local Linear Estimator.
Technical Report
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 highdimensional 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.rproject.org/web/packages/DDL/index.html.
Guo, Z. , Cevid, D., and Bühlmann, P. (2021+).
Doubly Debiased Lasso: HighDimensional Inference under Hidden Confounding.
Annals of Statistics, 50 (3), 1320  1347.
Description: Performs causal mediation analysis for count and zeroinflated count data without or with a posttreatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment induced mediatoroutcome 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.
Cheng J., Cheng, N. F., Guo, Z., Gregorich, S., Ismail, A. I. and Gansky, S. A. (2018).
Mediation Analysis for Count and ZeroInflated Count Data.
Statistical Methods in Medical Research, 27(9), 27562774.
Guo, Z., Small, D. S., Gansky, S. A., and Cheng, J. (2018).
Mediation Analysis for Count and ZeroInflated Count Data without Sequential Ignorability.
Journal of the Royal Statistical Society: Series C, 67(2), 371394.
