Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
This work addresses a fundamental challenge in causal inference for researchers and practitioners dealing with complex data, though it is incremental as it builds on existing assumptions and methods.
The authors tackled the problem of recovering causal graphs in the presence of latent confounders and selection bias by introducing the iterative causal discovery (ICD) algorithm, which requires significantly fewer conditional independence tests and learns more accurate graphs compared to existing methods like FCI, FCI+, and RFCI.
We present a sound and complete algorithm, called iterative causal discovery (ICD), for recovering causal graphs in the presence of latent confounders and selection bias. ICD relies on the causal Markov and faithfulness assumptions and recovers the equivalence class of the underlying causal graph. It starts with a complete graph, and consists of a single iterative stage that gradually refines this graph by identifying conditional independence (CI) between connected nodes. Independence and causal relations entailed after any iteration are correct, rendering ICD anytime. Essentially, we tie the size of the CI conditioning set to its distance on the graph from the tested nodes, and increase this value in the successive iteration. Thus, each iteration refines a graph that was recovered by previous iterations having smaller conditioning sets -- a higher statistical power -- which contributes to stability. We demonstrate empirically that ICD requires significantly fewer CI tests and learns more accurate causal graphs compared to FCI, FCI+, and RFCI algorithms (code is available at https://github.com/IntelLabs/causality-lab).