Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data

• URL: http://arxiv.org/abs/2312.06091v2
• Date: Sat, 9 Mar 2024 11:38:55 GMT
• Title: Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
• Authors: Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash
• Abstract summary: We study the problem of identifying the unknown intervention targets in structural causal models. In the presence of latent confounders, the intervention targets among the observed variables cannot be determined uniquely. Our approach improves upon the state of the art as the returned candidate set is always a subset of the true intervention targets.
• Score: 27.38242139321935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of identifying the unknown intervention targets in structural causal models where we have access to heterogeneous data collected from multiple environments. The unknown intervention targets are the set of endogenous variables whose corresponding exogenous noises change across the environments. We propose a two-phase approach which in the first phase recovers the exogenous noises corresponding to unknown intervention targets whose distributions have changed across environments. In the second phase, the recovered noises are matched with the corresponding endogenous variables. For the recovery phase, we provide sufficient conditions for learning these exogenous noises up to some component-wise invertible transformation. For the matching phase, under the causal sufficiency assumption, we show that the proposed method uniquely identifies the intervention targets. In the presence of latent confounders, the intervention targets among the observed variables cannot be determined uniquely. We provide a candidate intervention target set which is a superset of the true intervention targets. Our approach improves upon the state of the art as the returned candidate set is always a subset of the target set returned by previous work. Moreover, we do not require restrictive assumptions such as linearity of the causal model or performing invariance tests to learn whether a distribution is changing across environments which could be highly sample inefficient. Our experimental results show the effectiveness of our proposed algorithm in practice.

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