# Papers

## Technical Reports

Technical Reports

- Mattei, A., Mealli, F. and
**Ding, P.**(2020+). Assessing causal effects in the presence of treatment switching through principal stratification. - Basse, G.,
**Ding, P.**, Feller, A. and Toulis, P. (2019+). Randomization tests for peer effects in group formation experiments. **Ding, P.**(2019+). Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance.- Lei, L. and
**Ding, P.**(2019+). Regression adjustment in completely randomized experiments with a diverging number of covariates. **Ding, P.**and VanderWeele, T. J. (2016+). The differential geometry of homogeneous spaces across effect scales.

## Refereed Journal Publications

Refereed Journal Publications

- Wu, J. and
**Ding, P.**(2020+). Randomization tests for weak null hypotheses in randomized experiments.*Journal of the American Statistical Association*, in press. - Li, X. and
**Ding, P.**(2020). Rerandomization and regression adjustment.*Journal of the Royal Statistical Society, Series B*, 82, 241-268. [slides][R code] - D'Amour, A.,
**Ding, P.**, Feller, A., Lei, L. and Sekhon, J. (2019+) Overlap in observational studies with high-dimensional covariates.*Journal of Econometrics*, in press. - Lu, J., Zhang, Y. and
**Ding, P.**(2019+). Sharp bounds on the relative treatment effect for ordinal outcomes.*Biometrics*, in press. [slides] - Jiang, Z. and
**Ding, P.**(2020). Measurement errors in the binary instrumental variable model.*Biometrika*, 107, 238-245. - Yang, S. and
**Ding, P.**(2019+). Combining multiple observational data sources to estimate causal effects.*Journal of the American Statistical Association*, in press. [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 of the United States of America*, 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.

- 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).*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*.*