On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition

• URL: http://arxiv.org/abs/2405.07220v1
• Date: Sun, 12 May 2024 08:48:37 GMT
• Title: On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition
• Authors: Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee,
• Abstract summary: We define and characterize the local independence relationship that holds in a specific set of joint assignments of parental variables. We propose a novel method, coined neural contextual decomposition (NCD), which learns such partition by imposing each set to induce CSSI.
• Score: 26.34622544479565
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships especially between a variable and its parents, which will be called the local independence relationships. One of the most widely studied local relationships is Context-Specific Independence (CSI), which holds in a specific assignment of conditioned variables. However, its applicability is often limited since it does not allow continuous variables: data conditioned to the specific value of a continuous variable contains few instances, if not none, making it infeasible to test independence. In this work, we define and characterize the local independence relationship that holds in a specific set of joint assignments of parental variables, which we call context-set specific independence (CSSI). We then provide a canonical representation of CSSI and prove its fundamental properties. Based on our theoretical findings, we cast the problem of discovering multiple CSSI relationships in a system as finding a partition of the joint outcome space. Finally, we propose a novel method, coined neural contextual decomposition (NCD), which learns such partition by imposing each set to induce CSSI via modeling a conditional distribution. We empirically demonstrate that the proposed method successfully discovers the ground truth local independence relationships in both synthetic dataset and complex system reflecting the real-world physical dynamics.

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