9 papers
Estimating the Average Treatment Effect under Limited Overlap via Polynomial Approximation and Extrapolation
Shunichiro Orihara, Sho Komukai, Fan Li
Estimating the average treatment effect (ATE) remains a fundamental challenge in observational studies in the presence of poor or limited covariate overlap. Although the inverse pr…
Efficient Bayesian Inference in the Cox Model via Rank-Ordered Likelihood
Tomohiro Ohigashi, Shunichiro Orihara, Shonosuke Sugasawa
In Bayesian inference for the Cox proportional hazards model, modeling the baseline hazard function is challenging. Recently, direct Bayesian inference using the partial likelihood…
On the Conservativeness of Robust Variance Estimators in Propensity Score Weighted Cox Models
Hiroya Morita, Shunichiro Orihara, Fumitaka Shimizu +1
In propensity score weighted analysis, robust variance that does not account for weight estimation is commonly used. In propensity score weighted Cox models (CoxPSW), the robust va…
Bayesian Doubly Robust Causal Inference via Posterior Coupling
Shunichiro Orihara, Tomotaka Momozaki, Shonosuke Sugasawa
Bayesian doubly robust (DR) causal inference faces a fundamental dilemma: joint modeling of outcome and propensity score suffers from the feedback problem where outcome information…
Sample size re-estimation in blinded hybrid-control design using inverse probability weighting
Masahiro Kojima, Shunichiro Orihara, Keisuke Hanada +1
With the increasing availability of data from historical studies and real-world data sources, hybrid control designs that incorporate external data into the evaluation of current s…
General Bayesian inference for causal effects using covariate balancing procedure
Shunichiro Orihara, Tomotaka Momozaki, Tomoyuki Nakagawa
In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for…