Publications

Google Scholar Page

Books

• Ding, P. (2024) A first course in causal inference. Chapman & Hall. 

[slides for a short course, which can be used as an introduction to the book.]

[An old version on ArXiv] 

[R code on Harvard Dataverse] 

[Python code by Apoorva Lal] 

[purchase on Amazon]

• Ding, P. (2025) Linear model and extensions. Chapman & Hall. 

[R code on Harvard Dataverse]

Technical Reports

• Xu, Y., Zhao, A. and Ding, P. (2024+) Factorial difference-in-differences. 

• Kundu, S., Ding, P., Li, X. and Wang, J. (2024+) Sensitivity analysis for the test-negative design. 

• Yang, S. and Ding, P. (2024+) Two-phase rejective sampling. 

• Su, F., Mou, W., Ding, P. and Wainwright, M. (2023+) A decorrelation method for general regression adjustment in randomized experiments.

• Shen, D., Song, D., Ding, P. and Sekhon, J. (2023+) Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator.

• Lu, S., Jiang, Z. and Ding, P. (2023+) Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation.

• Yu, R. and Ding, P. (2023+). Balancing Weights for Causal Inference in Observational Factorial Studies.

• Gao, M. and Ding. P. (2023+). Causal inference in network experiments: regression-based analysis and design-based properties.

• Lu, S. and Ding, P. (2023+). Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding. [replication files on Harvard Dataverse]

• Su, F., Mou, W., Ding, P. and Wainwright, M. (2023+) When is the estimated propensity score better? High-dimensional analysis and bias correction.

• Mou, W., Ding, P., Wainwright, M. and Bartlett, P. (2023+) Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency.

• Shi, L. and Ding, P. (2022+). Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes.

• Zhang, M. and Ding, P. (2022+). Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding.

• Ding, P. and VanderWeele, T. J. (2016+). The differential geometry of homogeneous spaces across effect scales.

Refereed Journal Publications

• Zhang, Z., Ding, P., Zhou, W. and Wang, H. (2024+) With random regressors, least squares inference in robust to correlated errors with unknown correlation structure. Biometrika, in press. 

• Shi, L., Ding, P. and Wang, J. (2024+) Forward selection and post-selection inference in factorial designs. Annals of Statistics, in press.

• Zuo, S., Ghosh, D., Ding, P. and Yang, F. (2024+) Mediation analysis with the mediator and outcome missing not at random. Journal of the American Statistical Association, in press. [Fan Yang's presentation at the Online Causal Inference Seminar]

• Mattei, A., Ding, P.. Ballerini, V., and Mealli, F. (2024+). Assessing causal effects in the presence of treatment switching through principal stratification. Bayesian Analysis, in press.

• Zhao, A., Ding, P. and Li, F. (2024). Covariate adjustment in randomized experiments with missing outcomes and covariates. Biometrika, 111, 1413-1420. [slides (short version)]

• Zhao, A. and Ding, P. (2024). A randomization-based theory for preliminary testing of covariate balance in controlled trials. Statistics in Biopharmaceutical Research, 16, 498-511.

• Basse, G., Ding, P., Feller, A. and Toulis, P. (2024). Randomization tests for peer effects in group formation experiments. Econometrica, 92, 567-590.

• Zhao, A. and Ding, P. (2024). No star is good news: A unified look at rerandomization based on p-values from covariate balance tests. Journal of Econometrics, 241, 105724.

• Zhao, A. and Ding, P. (2024). To adjust or not to adjust? Estimating the average treatment effect in randomized experiments with missing covariates. Journal of the American Statistical Association, 119, 450-460. [slides: long version, short version] [video presented at Simons]

• Branson, Z., Li, X. and Ding, P. (2024). Power and sample size calculations for rerandomization. Biometrika, 111, 355-363.

• Shen, D., Ding, P., Sekhon, J. and Yu, B. (2023). Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data. Econometrica, 91, 2125-2154.

• Lin, Z., Ding, P., and Han, F. (2023). Estimation based on nearest neighbor matching: from density ratio to average treatment effect. Econometrica, 91, 2187-2217.

• Jiang, Z., Chen, S. and Ding, P. (2023). An instrumental variable method for point processes: generalised Wald estimation based on deconvolution. Biometrika, 110, 989-1008.

• Li, F., Ding, P. and Mealli, F. (2023). Bayesian causal inference: a critical review.  Philosophical Transactions of the Royal Society A, 381, 20220153. [Introduction slides and R code on Harvard Dataverse] [Fan Li's presentation at the Online Causal Inference Seminar]

• Zhao, A. and Ding, P. (2023). Covariate adjustment in multi-armed, possibly factorial experiments. Journal of the Royal Statistical Society, Series B, 85, 1-23. [slides]

• Lu, X., Liu, T., Liu, H. and Ding, P. (2023) Design-based theory for cluster rerandomization. Biometrika 110, 467-483.

• Jiang, Z., Yang, S. and Ding, P. (2022). Multiply robust estimation of causal effects under principal ignorability. Journal of the Royal Statistical Society, Series B, 84, 1423-1445. [replication files provided by Shu Yang]  [slides] [video talk (long version)] [video talk (short version)]

• Zhao, A. and Ding, P. (2022). Reconciling design-based and model-based causal inferences for split-plot experiments. Annals of Statistics, 50, 1170-1192.

• Zhao, A. and Ding, P. (2022). Regression-based causal inference with factorial experiments: estimands, model specifications, and design-based properties. Biometrika, 109, 799-815. [slides (short version)] [video talk (short version)]

• Su, F. and Ding, P. (2021). Model-assisted analyses of cluster-randomized experiments. Journal of the Royal Statistical Society, Series B, 83, 994-1015. [slides] [Dataset and R programs]

• Zhao, A. and Ding, P. (2021). Covariate-adjusted Fisher randomization tests for the average treatment effect. Journal of Econometrics, 225, 278-294. [slides]

• Lei, L. and Ding, P. (2021). Regression adjustment in completely randomized experiments with a diverging number of covariates. Biometrika, 108, 815-828.

• Jiang, Z. and Ding, P. (2021). Identification of causal effects within principal strata using auxiliary variables. Statistical Science, 36, 493-508. 

• Zeng, S., Li, F. and Ding, P. (2020). Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy. Journal of the Royal Statistical Society, Series A, 183, 1615-1635. [slides] [video talk] [replication files] [data]

• Wu, J. and Ding, P. (2021). Randomization tests for weak null hypotheses in randomized experiments. Journal of the American Statistical Association, 116, 1898-1913. [slides]  

• Li, X. and Ding, P. (2020). Rerandomization and regression adjustment. Journal of the Royal Statistical Society, Series B, 82, 241-268. [video of the presentation at the online causal inference seminar] [slides][R code]

• D'Amour, A., Ding, P., Feller, A., Lei, L. and Sekhon, J. (2021) Overlap in observational studies with high-dimensional covariates. Journal of Econometrics, 221, 644-654.

• Lu, J., Zhang, Y. and Ding, P. (2020).  Sharp bounds on the relative treatment effect for ordinal outcomes. Biometrics, 76, 664-669. [slides]

• Jiang, Z. and Ding, P. (2020). Measurement errors in the binary instrumental variable model. Biometrika, 107, 238-245.

• Yang, S. and Ding, P. (2020). Combining multiple observational data sources to estimate causal effects. Journal of the American Statistical Association, 115, 1540-1554. [slides]

• Ding, P. and Li, F. (2019). A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment. Political Analysis, 27, 605-615. [Slides]. Replication files at Harvard Dataverse

• Yang, S., Wang, L. and Ding, P. (2019). Causal inference with confounders missing not at random. Biometrika, 106, 875-888. [slides]

• Li, X., Ding, P. and Rubin, D. B. (2020). Rerandomization in 2^K factorial experiments. Annals of Statistics, 48, 43-63.

• Li, X., Ding, P. and Rubin, D. B. (2018). Asymptotic theory of rerandomization in treatment-control experiments. Proceedings of the National Academy of Sciences, 115, 9157-9162. [slides]

• Li, X., Ding, P., Lin, Q., Yang, D., and Liu, J. (2019). Randomization inference for peer effects. Journal of the American Statistical Association, 114, 1651-1664.

• Forastiere, L., Mattei, A., and Ding, P. (2018). Principal ignorability in mediation analysis: through and beyond sequential ignorability. Biometrika, 105, 979-986.

• Yang, F. and Ding, P. (2018). Using survival information in truncation by death problems without the monotonicity assumption. Biometrics, 74, 1232-1239.

• Ding, P. and Li, F. (2018). Causal inference: a missing data perspective. Statistical Science, 33, 214-237. [slides]

• Yang, S. and Ding, P. (2018). Asymptotic causal inference with observational studies trimmed by the estimated propensity scores. Biometrika 105, 487-493.

• Ding, P. and Keele, L. (2018) Rank tests in unmatched clustered randomized trials applied to a study of teacher training. Annals of Applied Statistics 12, 2151-2174.

• Jiang, Z. and Ding, P. (2018). Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi. Annals of Applied Statistics 12, 1831-1852.

• Ding, P., Feller, A. and Miratrix, L. (2019). Decomposing treatment effect variation. Journal of the American Statistical Association 114, 304-317. [slides]

• Ding, P. and Dasgupta, T. (2018). A randomization-based perspective on analysis of variance: a test statistic robust with respect to treatment effect heterogeneity. Biometrika, 105, 45-56. [slides] 

• Zhao, A., Ding, P., Mukerjee, R. and Dasgupta, T. (2018). Randomization-based causal inference from split-plot designs. Annals of Statistics, 46, 1876-1903.

• Lu, J., Ding, P. and Dasgupta, T. (2018). Treatment effects on ordinal outcomes: causal estimands and sharp bounds. Journal of Educational and Behavioral Statistics, 43, 540-567.

• Ding, P., VanderWeele, T. J. and Robins, J. (2017). Instrumental variables as bias amplifiers with general outcome and confounding. Biometrika, 104, 291-302. [slides]

• Li, X. and Ding, P. (2017). General forms of finite population central limit theorems with applications to causal inference. Journal of the American Statistical Association, 112, 1759-1769. [slides]

• Ding, P. and Lu, J. (2017). Principal stratification analysis using principal scores. Journal of the Royal Statistical Society, Series B, 79, 757-777. [Data sets and R programs] [slides]

• Ding, P. (2017). A Paradox from randomization-based causal inference (with discussion). Statistical Science, 32, 331-345. [slides]

       Ding, P. (2017). Rejoinder. Statistical Science, 32, 362-366.

• Ding, P. and VanderWeele, T. J. (2016). Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika, 103, 483-490. [slides]

• Miao, W., Ding, P. and Geng, Z. (2016). Identifiability of Normal and Normal mixture models with nonignorable missing data. Journal of the American Statistical Association, 111, 1673-1683. [slides]

• Ding, P. and VanderWeele, T. J. (2016). Sensitivity analysis without assumptions. Epidemiology, 27, 368-377. [slides]

       VanderWeele, T. J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167, 268-274. [with editorial comments by Localio et al., critiques by Ioannidis et al, and responses to critiques]

       Mathur, M. B., Ding, P., Riddell, C. A. and VanderWeele, T. J. (2018). Website and R package for computing E-values. Epidemiology, 29, e45-e47.

        VanderWeele, T. J., Ding, P., and Mathur, M. B. (2019). Technical considerations in the use of the E-value. Journal of Causal Inference, 20180007.

• Jiang, Z., Ding, P. and Geng, Z. (2016). Principal causal effect identification and surrogate endpoint evaluation by multiple trials. Journal of the Royal Statistical Society, Series B, 78, 829-848. [slides]

• Ding, P., Feller, A. and Miratrix, L. W. (2016). Randomization inference for treatment effect variation. Journal of the Royal Statistical Society, Series B, 78, 655-671.

• Ding, P. and Dasgupta, T. (2016). A potential tale of two by two tables from completely randomized experiments. Journal of the American Statistical Association, 111, 157-168. [slides]

• Ding, P. and Miratrix, L. W. (2015). To adjust or not to adjust? Sensitivity analysis of M-bias and butterfly-bias (with Professor Judea Pearl's comments, our response, and Felix Thoemmes' comments). Journal of Causal Inference, 3, 41-57. [slides]

        Ding, P. and Miratrix, L. W. (2015). Reply to Professor Pearl's comment. Journal of Causal Inference, 3, 251-252.

• Ding, P. and VanderWeele, T. J. (2014). Generalized Cornfield conditions for the risk difference.  Biometrika, 101, 971-977.

• Ding, P. and Geng, Z. (2014). Identifiability of subgroup causal effects in randomized experiments with nonignorable missing covariates. Statistics in Medicine, 33, 1121-1133. [slides] 

• Ding, P., Geng, Z., Yan, W. and Zhou, X. H. (2011). Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death. Journal of the American Statistical Association, 106, 1578-1591.

Other short papers

• Guo, W., Wang, S. L., Ding, P., Wang, Y., and Jordan, M. (2022). Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Transactions on Machine Learning Research.

• Ding, P. (2021). Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance. Journal of Causal Inference, 9, 1-8.

• Ding, P. (2021). The Frisch-Waugh-Lovell Theorem for Standard Errors. Statistics and Probability Letters, 168, 108945.

• Ding, P. and Miratrix, L. (2019). Model-free causal inference of binary experimental data. Scandinavian Journal of Statistics, 40, 200-214. 

• Ding, P., Li, X. and Miratrix, L. W. (2017). Bridging finite and super population causal inference. Journal of Causal Inference, 5, 2193-3685.  

• Jiang, Z. and Ding, P. (2017). The Directions of Selection Bias. Statistics and Probability Letters, 125, 104-109.

• Ding, P. and Blitzstein, J. K. (2018). On the Gaussian Mixture Representation for the Laplace Distribution. The American Statistician, 72, 172-174.

• Jiang, Z. and Ding, P. (2016). Robust Modeling Using Non-Elliptically Contoured Multivariate t Distributions. Journal of Statistical Planning and Inference, 177, 50-63.  

• Ding, P. (2016). On the Conditional Distribution of the Multivariate t Distribution. The American Statistician, 70, 293-295.

• Li, X. and Ding, P. (2016). Exact Confidence Intervals for the Average Causal Effect on a Binary Outcome. Statistics in Medicine, 35, 957-960.  

• Lu, J., Ding, P. and Dasgupta, T. (2015). Construction of Alternative Hypotheses for Evaluation of Randomization Tests With Ordinal Outcomes. Statistics and Probability Letters, 107, 348-355.

• Jiang, Z., Ding, P. and Geng, Z. (2015). Qualitative Evaluation of Associations by the Transitivity of the Association Signs. Statistica Sinica, 25, 1065-1079.

• Chen, H., Ding, P. Geng, Z. and Zhou, X. H. (2015). Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes. ACM Transactions on Intelligent Systems and Technology, 7 article 19.

• Ding, P. (2014). Bayesian Robust Inference of Sample Selection Using Selection-t Models. Journal of Multivariate Analysis, 124, 451-464.

• Ding, P. (2014). Three Occurrences of the Hyperbolic-Secant Distribution. The American Statistician, 68, 32-35.

• Yan, W., Ding, P., Geng, Z. and Zhou, X. H. (2011). Identifiability of Causal Effects on a Binary Outcome Within Principal Strata. Frontiers of Mathematics in China, 6, 1249-1263.

Comments

• Ding, P. (2023). Comment on "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment" by Imai et al. Journal of the Royal Statistical Society, Series A, 186, 195-198. [slides]

• Ding, P. (2022). Comment on "Assumption-lean inference for generalised linear model parameters" by Vansteelandt and Dukes. Journal of the Royal Statistical Society, Series B, 84, 691-693. [slides]

• Ding, P. and Guo, T. (2023). Posterior Predictive Propensity Scores and p-Values. Comment on Rosenbaum and Rubin (1983), reprinted in Observational Studies, 9, 3-18. [replication package]

• Ding, P. and Feller, A. (2016). Comment on "Causal Inference Using Invariant Prediction: Identification and Confidence Intervals" by Peters, J., Buehlmann, P. and Meinshausen, N. Journal of the Royal Statistical Society, Series B, 78, 994-995.

• Ding, P. (2016). Comment on "Perils and Potentials of Self-Selected Entry to Epidemiological Studies and Surveys" by Neils Keiding Thomas A. Louis. Journal of the Royal Statistical Society, Series A, 179, 355-356.

• Poole, C., Shrier, I., Ding, P. and VanderWeele, T. J. (2016). Response to the letter by Schmidt, Dudbridge and Groenwold. Epidemiology, 27, e12.

• Ding, P. (2014). Letter to the Editor: Comment on "A Paradoxical Result in Estimating Regression Coefficients" by Tarpey et al (with comments by R. Dennis Cook, Liliana Forzani and Adam Rothman and rejoinder). The American Statistician, 68, 316.

        Ding, P. (2015). Response to Cook et al.'s Comment. The American Statistician 69, 255-256.