SurvCaus : Representation Balancing for Survival Causal Inference
- URL: http://arxiv.org/abs/2203.15672v1
- Date: Tue, 29 Mar 2022 15:33:55 GMT
- Title: SurvCaus : Representation Balancing for Survival Causal Inference
- Authors: Ayoub Abraich, Agathe Guilloux, Blaise Hanczar
- Abstract summary: In many pathologies, the outcome of interest is a (possibly censored) survival time.
Our paper proposes theoretical guarantees for a representation balancing framework applied to counterfactual inference in a survival setting.
- Score: 3.4161707164978137
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Individual Treatment Effects (ITE) estimation methods have risen in
popularity in the last years. Most of the time, individual effects are better
presented as Conditional Average Treatment Effects (CATE). Recently,
representation balancing techniques have gained considerable momentum in causal
inference from observational data, still limited to continuous (and binary)
outcomes. However, in numerous pathologies, the outcome of interest is a
(possibly censored) survival time. Our paper proposes theoretical guarantees
for a representation balancing framework applied to counterfactual inference in
a survival setting using a neural network capable of predicting the factual and
counterfactual survival functions (and then the CATE), in the presence of
censorship, at the individual level. We also present extensive experiments on
synthetic and semisynthetic datasets that show that the proposed extensions
outperform baseline methods.
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