{
    "byline": null,
    "dir": null,
    "excerpt": "Theoretically, models should be able to predict on out-of-distribution data if their understanding of causal relationships is correct. The toy problem they use in this paper is that of predicting temperature from altitude. If a model is trained on data from Switzerland, the model should ideally be able to correctly predict on data from the Netherlands, even though it hasn\u2019t seen elevations that low before.",
    "length": 1514,
    "siteName": null,
    "title": "A meta-transfer objective for learning to disentangle causal mechanisms"
}