  Adjusting for latent covariates is crucial for estimating causal effects from
observational textual data. Most existing methods only account for confounding
covariates that affect both treatment and outcome, potentially leading to
biased causal effects. This bias arises from insufficient consideration of
non-confounding covariates, which are relevant only to either the treatment or
the outcome. In this work, we aim to mitigate the bias by unveiling
interactions between different variables to disentangle the non-confounding
covariates when estimating causal effects from text. The disentangling process
ensures covariates only contribute to their respective objectives, enabling
independence between variables. Additionally, we impose a constraint to balance
representations from the treatment group and control group to alleviate
selection bias. We conduct experiments on two different treatment factors under
various scenarios, and the proposed model significantly outperforms recent
strong baselines. Furthermore, our thorough analysis on earnings call
transcripts demonstrates that our model can effectively disentangle the
variables, and further investigations into real-world scenarios provide
guidance for investors to make informed decisions.
