Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism
This work addresses a fundamental problem in causal discovery for researchers and practitioners, offering a method to handle bivariate cases where traditional approaches fail, though it appears incremental in bridging existing theories.
The paper tackles the challenge of reconciling two causal inference research directions—conditional independence and independence of cause and mechanism—by studying latent variables like instrumental variables and hidden common causes. It proposes a novel algorithm for inferring causal relationships between two variables, showing competitive empirical accuracy compared to state-of-the-art methods on simulated data and a benchmark of cause-effect pairs.
Causal inference methods based on conditional independence construct Markov equivalent graphs, and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that the methods based on the independence of cause and mechanism, indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.