Intervene, Calibrate, Discover: Learning Causal Structure from Pixels

Learning high-level causal variables from low-level observations is a central problem for artificial intelligence (Schölkopf et al., 2021). With known node semantics but an unknown graph, readout errors can misalign model settings with environment intervention values. With encoder and decoder frozen, verified values from fixed round-robin interventions train dimension-wise invertible calibration; after freezing it, active experiments orient edges, local mechanisms predict consequences, and inverse calibration supports decoding. Across Domino, Pick-and-Place, Balance, Gears, CAUSAL3D, and CausalWorld, we test graph discovery, intervention consequences, and counterfactual images. Controlled domino experiments show that interventions resolve orientation ambiguity left by equal additional observations. In a matched-data domino control, calibration reduces the mean error in predicting how changing an intervention alters other variables within the same factual context, relative to an uncalibrated model.