6 citations · 7 across the 5 of their papers we have counts for
5 papers
Causal Inference: A Missing Data Perspective
Peng Ding, Fan Li
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal…
Instrumental variables as bias amplifiers with general outcome and confounding
Peng Ding, Tyler VanderWeele, James Robins
Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outc…
General forms of finite population central limit theorems with applications to causal inference
Xinran Li, Peng Ding
Frequentists' inference often delivers point estimators associated with confidence intervals or sets for parameters of interest. Constructing the confidence intervals or sets requi…
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…