Leveraging directed causal discovery to detect latent common causes

Abstract

The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from uncontrolled data have been proposed. However, most of these deal only with purely directed causal relationships and cannot detect latent common causes. Here, we devise a general method which takes a purely directed causal discovery algorithm and modifies it so that it can also detect latent common causes. The identifiability of the modified algorithm depends on the identifiability of the original, as well as an assumption that the strength of noise be relatively small. We apply our method to two directed causal discovery algorithms, the Information Geometric Causal Inference of (Daniusis et al., 2010) and the Kernel Conditional Deviance for Causal Inference of (Mitrovic, Sejdinovic, & Teh, 2018), and extensively test on synthetic data-detecting latent common causes in additive, multiplicative and complex noise regimes-and on real data, where we are able to detect known common causes. In addition to detecting latent common causes, our experiments demonstrate that both modified algorithms preserve the performance of the original directed algorithm in distinguishing directed causal relations.

Latent Causal Invariant Model

“…The Hoyer et al (2008); Janzing, Peters, Mooij and Schölkopf (2012) extend to the additive noise model (ANM) and other causal discovery assumptions. Although the Lee et al (2019) relaxed the constraints put on the causal structure, it required the latent noise is with small strength, which does not match with many realistic scenarios, such as the structural MRI of Alzheimer's Disease considered in our experiment. The works which also based on the independent component analysis (ICA), i.e., the latent variables are (conditionally) independent, include Davies (2004); Eriksson and Koivunen (2003); recently, a series of works extend the above results to deep nonlinear ICA (Hyvarinen and Morioka, 2016;Hyvärinen et al, 2019;Khemakhem, Monti, Kingma and Hyvärinen, 2020;Teshima et al, 2020).…”

Estimating Categorical Counterfactuals via Deep Twin Networks

“…This is because causal inference-unlike statistical inference from observational data-allows one to understand the impact of interventions and answer counterfactual queries. Recently there has been much interest in using machine learning tools to estimate causal inference queries (Schwab, Linhardt, and Karlen 2018;Alaa, Weisz, and Van Der Schaar 2017;Shi, Blei, and Veitch 2019;Pawlowski, Castro, and Glocker 2020;Shalit, Johansson, and Sontag 2016;Perov et al 2020;Dhir and Lee 2020;Lee et al 2019). However, most of these focus on interventional queries, such as the conditional average and individual treatment effects.…”