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Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
Learning causal effects from observational data greatly benefits a varie...
04/30/2019 ∙ by Xin Du, et al. ∙
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Double Robust Representation Learning for Counterfactual Prediction
Causal inference, or counterfactual prediction, is central to decision m...
10/15/2020 ∙ by Shuxi Zeng, et al. ∙
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A tutorial comparing different covariate balancing methods with an application evaluating the causal effect of exercise on the progression of Huntington's Disease
Randomized controlled trials are the gold standard for measuring the cau...
10/19/2020 ∙ by Andreas Markoulidakis, et al. ∙
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PSweight: An R Package for Propensity Score Weighting Analysis
Propensity score weighting is an important tool for causal inference and...
10/18/2020 ∙ by Tianhui Zhou, et al. ∙
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Permutation Weighting
This work introduces permutation weighting: a weighting estimator for ob...
01/04/2019 ∙ by David Arbour, et al. ∙
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Learning Causal Models Online
Predictive models – learned from observational data not covering the com...
06/12/2020 ∙ by Khurram Javed, et al. ∙
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The Counterfactual χ-GAN
Causal inference often relies on the counterfactual framework, which req...
01/09/2020 ∙ by Amelia J. Averitt, et al. ∙
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Counterfactual Representation Learning with Balancing Weights
10/23/2020 ∙ by Serge Assaad, et al. ∙
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A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate on the complementary roles of feature balancing and weight adjustments. Using these concepts, we then develop an algorithm for flexible, scalable and accurate estimation of causal effects. Finally, we show how the learned weighted representations may serve to facilitate alternative causal learning procedures with appealing statistical features. We conduct an extensive set of experiments on both synthetic examples and standard benchmarks, and report encouraging results relative to state-of-the-art baselines.
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