Identifying Structural Biases from Causal Mechanism Shifts
For researchers in causal inference, this work provides a method to detect structural biases from mechanism shifts, addressing a common violation of assumptions in practice.
This paper addresses the problem of identifying hidden confounding and selection biases in causal discovery when i.i.d. assumptions are violated. The proposed StruBI algorithm accurately recovers affected variable sets and bias types, outperforming state-of-the-art methods by a wide margin on synthetic and real-world data.
Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In practice, these assumptions are often violated, leading to inaccurate inference. In this paper, we study how to identify hidden confounding and selection biases from causal mechanism shifts. In particular, we show that structural biases lead to dependent mechanism shifts. That is, by considering for which variables the mechanisms change given data from different environments, we can tell which variables are unbiased, which are subject to hidden confounding, and which are undergoing selection bias. We formalize this into an empirically testable criterion based on mutual information, and show under which conditions it identifies structural biases. To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm. Experiments on synthetic and real-world data show that StruBI works well in practice, accurately recovering affected variable sets and types of biases, outperforming the state-of-the-art by a wide margin.