The tetrad constraint is widely used to test whether four observed variables are conditionally independent given a latent variable, based on the fact that if four observed variables following a linear model are mutually independent after conditioning on an unobserved variable, then products of covariances of any two different pairs of these four variables are equal. It is an important tool for discovering a latent common cause or distinguishing between alternative linear causal structures. However, the classical tetrad constraint fails in nonlinear models because the covariance of observed variables cannot capture nonlinear association. In this paper, we propose a generalized tetrad constraint, which establishes a testable implication for conditional independence given a latent variable in nonlinear and nonparametric models. In linear models, this constraint implies the classical tetrad constraint; in nonlinear models, it remains a necessary condition for conditional independence but the classical tetrad constraint no longer is. Based on this constraint, we further propose a formal test, which can control type I error and has power approaching unity under certain conditions. We illustrate the proposed approach via simulations and two real data applications on mental ability tests and on moral attitudes towards dishonesty.

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