2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning
Xiaokang Luo, Tirthankar Dasgupta, Minge Xie +1
The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomiz…
Causal Inference from Possibly Unbalanced Split-Plot Designs: A Randomization-based Perspective
Rahul Mukerjee, Tirthankar Dasgupta
Split-plot designs find wide applicability in multifactor experiments with randomization restrictions. Practical considerations often warrant the use of unbalanced designs. This pa…
Causal Inference in Rebuilding and Extending the Recondite Bridge between Finite Population Sampling and Experimental Design
Rahul Mukerjee, Tirthankar Dasgupta, Donald B. Rubin
This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated…
Causal inference from factorial designs using the potential outcomes model
Tirthankar Dasgupta, Natesh S. Pillai, Donald B. Rubin
A framework for causal inference from two-level factorial designs is proposed. The framework utilizes the concept of potential outcomes that lies at the center stage of causal infe…