7 papers
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta +1
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which ca…
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Mariyam Khan, Shohei Shimizu, Thong Pham
We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM). Existing methods that…
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables
Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi +3
Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to…
Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
Tadahisa Okuda, Shohei Shimizu, Thong Pham +2
Causal discovery has achieved substantial theoretical progress, yet its deployment in large-scale longitudinal systems remains limited. A key obstacle is that operational data are…
Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data
Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
We address the problem of inferring the causal direction between a continuous variable and a discrete variable from observational data. For the model , we adopt th…
Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between…
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
Takashi Nicholas Maeda, Shohei Shimizu
This paper proposes two methods for causal additive models with unobserved variables (CAM-UV). CAM-UV assumes that the causal functions take the form of generalized additive models…