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Causal representation learning has been proposed to encode relationshipsbetween factors presented in the high dimensional data. However, existingmethods suffer from merely using a large amount of labeled data and ignore thefact that samples generated by the same causal mechanism follow the same causalrelationships. In this paper, we seek to explore such information by leveragingdo-operation to reduce supervision strength. We propose a framework thatimplements do-operation by swapping latent cause and effect factors encodedfrom a pair of inputs. Moreover, we also identify the inadequacy of existingcausal representation metrics empirically and theoretically and introduce newmetrics for better evaluation. Experiments conducted on both synthetic and realdatasets demonstrate the superiorities of our method compared withstate-of-the-art methods.

Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength