COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
CoRR(2023)
摘要
Estimation of temporal counterfactual outcomes from observed history is
crucial for decision-making in many domains such as healthcare and e-commerce,
particularly when randomized controlled trials (RCTs) suffer from high cost or
impracticality. For real-world datasets, modeling time-dependent confounders is
challenging due to complex dynamics, long-range dependencies and both past
treatments and covariates affecting the future outcomes. In this paper, we
introduce Counterfactual Self-Supervised Transformer (COSTAR), a novel approach
that integrates self-supervised learning for improved historical
representations. We propose a component-wise contrastive loss tailored for
temporal treatment outcome observations and explain its effectiveness from the
view of unsupervised domain adaptation. COSTAR yields superior performance in
estimation accuracy and generalization to out-of-distribution data compared to
existing models, as validated by empirical results on both synthetic and
real-world datasets.
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关键词
improved temporal counterfactual estimation,learning,self-supervised
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