作者：Qu, Tianyi; Du, Jiangchuan; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
摘要：Randomized experiments have been the gold standard for drawing causal inference. The conventional model-based approach has been one of the most popular methods of analysing treatment effects from randomized experiments, which is often carried out through inference for certain model parameters. In this paper, we provide a systematic investigation of model-based analyses for treatment effects under the randomization-based inference framework. This framework does not impose any distributional ass...
作者：Smucler, E.; Robins, J. M.; Rotnitzky, A.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
摘要：This paper examines the construction of confidence sets for parameters defined as linear functionals of a function of $ W $ and $ X $ whose conditional mean given $ Z $ and $ X $ equals the conditional mean of another variable $ Y $ given $ Z $ and $ X $. Many estimands of interest in causal inference can be expressed in this form, including the average treatment effect in proximal causal inference and treatment effect contrasts in instrumental variable models. We derive a necessary condition ...
