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In causal inference, it is a fundamental task to estimate the causal effectfrom observational data. However, latent confounders pose major challenges incausal inference in observational data, for example, confounding bias andM-bias. Recent data-driven causal effect estimators tackle the confounding biasproblem via balanced representation learning, but assume no M-bias in thesystem, thus they fail to handle the M-bias. In this paper, we identify achallenging and unsolved problem caused by a variable that leads to confoundingbias and M-bias simultaneously. To address this problem with co-occurringM-bias and confounding bias, we propose a novel Disentangled LatentRepresentation learning framework for learning latent representations fromproxy variables for unbiased Causal effect Estimation (DLRCE) fromobservational data. Specifically, DLRCE learns three sets of latentrepresentations from the measured proxy variables to adjust for the confoundingbias and M-bias. Extensive experiments on both synthetic and three real-worlddatasets demonstrate that DLRCE significantly outperforms the state-of-the-artestimators in the case of the presence of both confounding bias and M-bias.

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference