{"paper":{"title":"Towards a Learning Theory of Cause-Effect Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Bernhard Sch\\\"olkopf, David Lopez-Paz, Ilya Tolstikhin, Krikamol Muandet","submitted_at":"2015-02-09T08:49:26Z","abstract_excerpt":"We pose causal inference as the problem of learning to classify probability distributions. In particular, we assume access to a collection $\\{(S_i,l_i)\\}_{i=1}^n$, where each $S_i$ is a sample drawn from the probability distribution of $X_i \\times Y_i$, and $l_i$ is a binary label indicating whether \"$X_i \\to Y_i$\" or \"$X_i \\leftarrow Y_i$\". Given these data, we build a causal inference rule in two steps. First, we featurize each $S_i$ using the kernel mean embedding associated with some characteristic kernel. Second, we train a binary classifier on such embeddings to distinguish between causa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02398","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}