Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction (CVPR 2023)

paper: https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.pdf

supple: https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Causally-Aware_Intraoperative_Imputation_CVPR_2023_supplemental.pdf

MAIN FUNCTIONS

function [g_skeleton, g_inv, gns, SP] = nonsta_cd_new(X,cond_ind_test,c_indx,maxFanIn,alpha, Type, pars)

EXAMPLE

CaDAG: example_for_seclect.m

INPUT: DATA.xlsx (normalized)

OUTPUT: matrix g0 as a DAG structure

"g0(i,j)=1" means the i-th attribute is the parent node of the j-th attribute.

REFERENCE

1. Li X, Qian X, Liang L, Kong L, Dong Q, Chen J, Liu D, Yao X, Fu Y. Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 15681-15690).
2. Zhang, K., Huang, B., Zhang, J., Glymour, C., Scholkopf, B.. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017.
3. Huang, B., Zhang, K., Zhang, J., Glymour, C., Scholkopf, B. Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017.
4. Huang, B., Zhang, K., Zhang, J., Ramsey, J., Sanchez-Romero, R., Glymour, C., Scholkopf, B.. Causal Discovery from Heterogeneous/Nonstationary Data. JMLR, 21(89), 2020.