Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent ConfoundersDownload PDF

Published: 09 Nov 2021, Last Modified: 05 May 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: causal discovery, linear non-gaussian models, faithfulness, confounding, topology
TL;DR: We refine causal identifiability results in the linear non-Gaussian setting, allowing for the presence of latent confounders.
Abstract: If causal relationships are linear and acyclic and noise terms are independent and Gaussian, causal orientation is not identified from observational data --- even if faithfulness is satisfied (Spirtes et al., 2002). Shimizu et al. (2006) showed that acyclic, linear, {\bf non}-Gaussian (LiNGAM) causal models {\em are} identified from observational data, so long as no latent confounders are present. That holds even when faithfulness fails. Genin and Mayo-Wilson (2020) refine that result: not only are causal relationships identified, but causal orientation is {\em statistically decidable}. That means that for every $\epsilon>0,$ there is a method that converges in probability to the correct orientation and, at every sample size, outputs an incorrect orientation with probability less than $\epsilon.$ These results naturally raise questions about what happens in the presence of latent confounders. Hoyer et al. (2008) and Salehkaleybar et al. (2020) show that, although the causal model is not uniquely identified, causal orientation among observed variables is identified in the presence of latent confounders, so long as faithfulness is satisfied. This paper refines these results: although it is possible to converge to the right orientation in the limit, causal orientation is no longer statistically decidable---it is not possible to converge to the correct orientation with finite-sample bounds on the probability of orientation errors, even if faithfulness is satisfied. However, that limiting result suggests several adjustments to the LiNGAM model that may recover decidability.
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