Related work 24

• Mei, Z., Fan, Q., and Guo, Z. (2026).
  Identification and Robust Inference for Multiple Treatment Effects with Possibly Invalid Instruments.
  Technical Report

• Heng, S.^#✉, Shen, Y.^#, Guo, Z.^✉ (2026).
  Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores.
  Technical Report

• Chang, T. H., Guo, Z., and Malinsky, D. (2026).
  Post-selection inference for causal effects after causal discovery.
  Biometrika, 113(1), asaf073.

• Guo, Z., Zheng, M., and Bühlmann, P. (2026).
  Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
  Journal of Machine Learning Research, to appear.

• Scheidegger, C., Guo, Z., and Bühlmann, P. (2026).
  Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
  Electronic Journal of Statistics, 20(1), 718–770.

• Koo, T. and Guo, Z. (2025).
  Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts.
  Technical Report

• Zheng, M., Bonvini, M.^✉, and Guo, Z.^✉ (2025).
  Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length
  Technical Report

• Gu, Y., Fang, C., Xu, Y., Guo, Z., and Fan, J. (2025).
  Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
  Technical Report

• Scheidegger, C., Guo, Z., and Bühlmann, P. (2025).
  Spectral deconfounding for high-dimensional sparse additive models
  ACM/IMS Journal of Data Science, 2(1), Article 1, 52 pages.

• *Fan, Q., Guo, Z., and Mei, Z. (2025).
  A Heteroscedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
  Journal of Business & Economic Statistics, 43(2), 413-422.

• Carl, D., Emmenegger, C., Bühlmann, P., and Guo, Z. (2025).
  TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R
  Journal of Statistical Software, 114(7), 1-21. [Code]

• Kang, H., Guo, Z., Liu, Z., and Small, D. (2025).
  Identification and Inference with Invalid Instruments
  Annual Review of Statistics and Its Application, 12, 385–405.

• Wang, Z.^#, Hu, Y.^#, Bühlmann, P.^✉, and Guo, Z.^✉ (2024).
  Causal Invariance Learning via Efficient Nonconvex Optimization
  Technical Report

• Zhu, J., Zhang, J., Guo, Z., and Heng, S. (2023).
  Randomization-Based Inference for Average Treatment Effects in Inexactly Matched Observational Studies.
  Technical Report

• *Fan, Q., Guo, Z., Mei, Z., and Zhang, C. (2023).
  Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity.
  Technical report

• Guo, Z. (2023).
  Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling.
  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 85(3), 959-985. [Code]

• Koo, T., Lee, Y., Small, D., Guo, Z. (2023).
  RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
  Observational Studies, 9(4), 97-120. [Code]

• Guo, Z.^#✉, Ćevid, D.^#, and Bühlmann, P. (2022).
  Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding.
  Annals of Statistics, 50 (3), 1320 - 1347. [Code]

• 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. [Code]

• 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. [Code]

• 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. [Code]

• 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. [Code]

• 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. [Code]

• Guo, Z., Cheng, J., Lorch, S. A., and Small, D. S. (2014).
  Using an Instrumental Variable to Test for Unmeasured Confounding.
  Statistics in Medicine, 33, 3528 - 3546. [Code]