10,181 research outputs found
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
The dramatically growing availability of observational data is being
witnessed in various domains of science and technology, which facilitates the
study of causal inference. However, estimating treatment effects from
observational data is faced with two major challenges, missing counterfactual
outcomes and treatment selection bias. Matching methods are among the most
widely used and fundamental approaches to estimating treatment effects, but
existing matching methods have poor performance when facing data with high
dimensional and complicated variables. We propose a feature selection
representation matching (FSRM) method based on deep representation learning and
matching, which maps the original covariate space into a selective, nonlinear,
and balanced representation space, and then conducts matching in the learned
representation space. FSRM adopts deep feature selection to minimize the
influence of irrelevant variables for estimating treatment effects and
incorporates a regularizer based on the Wasserstein distance to learn balanced
representations. We evaluate the performance of our FSRM method on three
datasets, and the results demonstrate superiority over the state-of-the-art
methods.Comment: Proceedings of the 29th ACM International Conference on Information
and Knowledge Management (CIKM '20
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
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