3 papers
stat.ME2026
Robustness and Efficiency of Rosenbaum's Rank-based Estimator in Randomized Trials: A Design-based Perspective
Aditya Ghosh, Nabarun Deb, Bikram Karmakar +1
Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator propos…
stat.ME2025
Using a Two-Parameter Sensitivity Analysis Framework to Efficiently Combine Randomized and Non-randomized Studies
Ruoqi Yu, Bikram Karmakar, Jessica Vandeleest +1
Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold st…
stat.ME2025
Degree of Interference: A General Framework For Causal Inference Under Interference
Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi
One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is unaffected by t…