Doubly robust average treatment effect estimation for survival data
By: Byeonghee Lee, Joonsung Kang
Potential Business Impact:
Helps doctors predict patient survival better.
Considering censored outcomes in survival analysis can lead to quite complex results in the model setting of causal inference. Causal inference has attracted a lot of attention over the past few years, but little research has been done on survival analysis. Even for the only research conducted, the machine learning method was considered assuming a large sample, which is not suitable in that the actual data are high dimensional low sample size (HDLSS) method. Therefore, penalty is considered for numerous covariates, and the relationship between these covariates and treatment variables is reflected as a covariate balancing property score (CBPS). It also considers censored results. To this end, we will try to solve the above-mentioned problems by using penalized empirical likelihood, which considers both estimating equation and penalty. The proposed average treatment effect (ATE) estimator possesses the oracle property, exhibiting key characteristics such as double robustness for unbiasedness, sparsity in model selection, and asymptotic normality.
Similar Papers
Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
Methodology
Helps doctors find best treatment for patients.
Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
Methodology
Finds true effects from messy data.
Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
Machine Learning (CS)
Finds medicine that works even if patients quit.