ICML 2023 Papers

Causal disentanglement seeks a representation of data involving latent variables that are related via a causal model. A representation is identifiable if both the latent model and the transformation from latent to observed variables are unique. In this paper, we study observed variables that are a linear transformation of a linear latent causal model. Data from interventions are necessary for identifiability: if one latent variable is missing an intervention, we show that there exist distinct models that cannot be distinguished. Conversely, we show that a single intervention on each latent variable is sufficient for identifiability. Our proof uses a generalization of the RQ decomposition of a matrix that replaces the usual orthogonal and upper triangular conditions with analogues depending on a partial order on the rows of the matrix, with partial order determined by a latent causal model. We corroborate our theoretical results with a method for causal disentanglement. We show that the method accurately recovers a latent causal model on synthetic and semi-synthetic data and we illustrate a use case on a dataset of single-cell RNA sequencing measurements.

Causal structure learning can reveal the causal mechanism behind natural systems. It is well studied that the multiple domain data consisting of observational and interventional samples benefit causal identifiability. However, for non-stationary time series data, domain indexes are often unavailable, making it difficult to distinguish observational samples from interventional samples. To address these issues, we propose a novel Latent Intervened Non-stationary learning (LIN) method to make the domain indexes recovery process and the causal structure learning process mutually promote each other. We characterize and justify a possible faithfulness condition to guarantee the identifiability of the proposed LIN method. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms the baselines on causal structure learning for latent intervened non-stationary data.

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.