9 citations · 32 across the 16 of their papers we have counts for
11 papers · 1 filter
Policy evaluation and learning with partially identified utility under truncation by death
Zi Huang, Zhichao Jiang
Policy evaluation and learning aim to assess and optimize treatment assignment rules based on individual characteristics. A fundamental challenge arises when outcomes are truncated…
Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference
Zhichao Jiang, Peng Ding
Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit va…
Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation
Sizhu Lu, Zhichao Jiang, Peng Ding
Post-treatment variables often complicate causal inference. They appear in many scientific problems, including noncompliance, truncation by death, mediation, and surrogate endpoint…
An instrumental variable method for point processes: generalised Wald estimation based on deconvolution
Zhichao Jiang, Shizhe Chen, Peng Ding
Point processes are probabilistic tools for modeling event data. While there exists a fast-growing literature studying the relationships between point processes, it remains unexplo…
Multiply robust estimation of causal effects under principal ignorability
Zhichao Jiang, Shu Yang, Peng Ding
Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stra…
Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments
Zhichao Jiang, Kosuke Imai, Anup Malani
Two-stage randomized experiments are becoming an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignm…
Identification of Causal Effects Within Principal Strata Using Auxiliary Variables
Zhichao Jiang, Peng Ding
In causal inference, principal stratification is a framework for dealing with a posttreatment intermediate variable between a treatment and an outcome, in which the principal strat…
Discussion of "The Blessings of Multiple Causes" by Wang and Blei
Kosuke Imai, Zhichao Jiang
This commentary has two goals. We first critically review the deconfounder method and point out its advantages and limitations. We then briefly consider three possible ways to addr…
Measurement errors in the binary instrumental variable model
Zhichao Jiang, Peng Ding
Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumen…
Using Missing Types to Improve Partial Identification with Application to a Study of HIV Prevalence in Malawi
Zhichao Jiang, Peng Ding
Frequently, empirical studies are plagued with missing data. When the data are missing not at random, the parameter of interest is not identifiable in general. Without additional a…
Robust Modeling Using Non-Elliptically Contoured Multivariate t Distributions
Zhichao Jiang, Peng Ding
Models based on multivariate t distributions are widely applied to analyze data with heavy tails. However, all the marginal distributions of the multivariate t distributions are re…