This repository contains the datasets from the paper "iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects" (link) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. Instantaneous Temporal Causal3Ident - The Temporal Causal3DIdent dataset is a collection of 3D object shapes, which are observed under varying positions, rotations, lightning, and colors. Overall, we this dataset contains 7 (multidimensional) causal factors with instantaneous and temporal causal relations between them. The 7 shapes used are Armadillo, Bunny, Cow, Dragon, Head, Horse, Teapot. For more details on the dataset, see our GitHub repository. Causal Pinball - The Causal Pinball environment implements the simplified, real-world game dynamics of Pinball. This dataset considers 5 causal variables with instantaneous effects: the paddle position left, the paddle position right, the ball (velocity and position), the state of all bumpers, and the score. For more details on the dataset as well as the code to generate this dataset, see our GitHub repository.

{"references": ["Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. In Proceedings of the 39th International Conference on Machine Learning, ICML 2022.", "Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects. arXiv preprint arXiv:2206.06169"]}