32 citations · 35 across the 5 of their papers we have counts for
6 papers
Is the treatment beneficial? Causal Inference with Ordinal Outcomes: A Bayesian Solution
Rituparna Dey, Pradipta Sarkar, Tirthankar Dasgupta
Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to inte…
Optimal allocation of sample size for randomization-based inference from factorial designs
Arun Ravichandran, Nicole E. Pashley, Brian Libgober +1
Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. Howe…
Causal inference from treatment-control studies having an additional factor with unknown assignment mechanism
Nicole E. Pashley, Kristen B. Hunter, Katy McKeough +2
Consider a situation with two treatments, the first of which is randomized but the second is not, and the multifactor version of this. Interest is in treatment effects, defined usi…
Randomization-Based Causal Inference from Unbalanced 2^2 Split-Plot Designs
Anqi Zhao, Peng Ding, Tirthankar Dasgupta
Given two 2-level factors of interest, a 2^2 split-plot design} (a) takes each of the possible factorial combinations as a treatment, (b) identifies one factor as `whole-pl…
A conditional randomization test to account for covariate imbalance in randomized experiments
Jonathan Hennessy, Tirthankar Dasgupta, Luke Miratrix +2
We consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the obs…
Inference for deformation and interference in 3D printing
Arman Sabbaghi, Tirthankar Dasgupta, Qiang Huang +1
Additive manufacturing, or 3D printing, is a promising manufacturing technique marred by product deformation due to material solidification in the printing process. Control of prin…