43 citations · 354 across the 81 of their papers we have counts for
9 papers · 1 filter
Randomization Inference for Treatment Effect Variation
Peng Ding, Avi Feller, Luke Miratrix
Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation not explained by observed covariates. We p…
Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes
Hua Chen, Peng Ding, Zhi Geng +1
Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous pa…
To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias
Peng Ding, Luke Miratrix
"M-Bias," as it is called in the epidemiologic literature, is the bias introduced by conditioning on a pretreatment covariate due to a particular "M-Structure" between two latent f…
Qualitative Evaluation of Associations by the Transitivity of the Association Signs
Zhichao Jiang, Peng Ding, Zhi Geng
We say that the signs of association measures among three variables {X, Y, Z} are transitive if a positive association measure between the variable X and the intermediate variable…
Generalized Cornfield conditions for the risk difference
Peng Ding, Tyler J. VanderWeele
A central question in causal inference with observational studies is the sensitivity of conclusions to unmeasured confounding. The classical Cornfield condition allows us to assess…
A paradox from randomization-based causal inference
Peng Ding
Under the potential outcomes framework, causal effects are defined as comparisons between potential outcomes under treatment and control. To infer causal effects from randomized ex…