Distinguishing Cause from Effect Based on Exogeneity
This work addresses causal inference for researchers, offering a novel approach that is incremental by building on existing methods but with reduced assumptions.
The paper tackles the problem of distinguishing cause from effect in two-variable causal inference by proposing a new framework based on exogeneity, avoiding structural constraints of SEM-based methods, and validates it with synthetic and real data.
Recent developments in structural equation modeling have produced several methods that can usually distinguish cause from effect in the two-variable case. For that purpose, however, one has to impose substantial structural constraints or smoothness assumptions on the functional causal models. In this paper, we consider the problem of determining the causal direction from a related but different point of view, and propose a new framework for causal direction determination. We show that it is possible to perform causal inference based on the condition that the cause is "exogenous" for the parameters involved in the generating process from the cause to the effect. In this way, we avoid the structural constraints required by the SEM-based approaches. In particular, we exploit nonparametric methods to estimate marginal and conditional distributions, and propose a bootstrap-based approach to test for the exogeneity condition; the testing results indicate the causal direction between two variables. The proposed method is validated on both synthetic and real data.