Representation learning for treatment effect estimation from observational data
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
Representation learning for treatment effect estimation from observational data
L Yao, S Li, Y Li, M Huai, J Gao, A Zhang - Proceedings of the 32nd …, 2018 - dl.acm.org
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
Representation Learning for Treatment Effect Estimation from Observational Data
L Yao, S Li, Y Li, M Huai, J Gao… - Advances in Neural …, 2018 - proceedings.neurips.cc
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
[PDF][PDF] Representation Learning for Treatment Effect Estimation from Observational Data
L Yao, S Li, Y Li, M Huai, J Gao, A Zhang - mdhuai.github.io
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
[PDF][PDF] Representation Learning for Treatment Effect Estimation from Observational Data
L Yao, S Li, Y Li, M Huai, J Gao, A Zhang - papers.neurips.cc
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
[PDF][PDF] Representation Learning for Treatment Effect Estimation from Observational Data
L Yao, S Li, Y Li, M Huai, J Gao, A Zhang - scholar.archive.org
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
[PDF][PDF] Representation Learning for Treatment Effect Estimation from Observational Data
L Yao, S Li, Y Li, M Huai, J Gao, A Zhang - Advances in neural …, 2018 - par.nsf.gov
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …