  Current top-notch deep learning (DL) based vision models are primarily based
on exploring and exploiting the inherent correlations between training data
samples and their associated labels. However, a known practical challenge is
their degraded performance against "noisy" data, induced by different
circumstances such as spurious correlations, irrelevant contexts, domain shift,
and adversarial attacks. In this work, we incorporate this binary information
of "existence of noise" as treatment into image classification tasks to improve
prediction accuracy by jointly estimating their treatment effects. Motivated
from causal variational inference, we propose a transformer-based architecture,
Treatment Learning Causal Transformer (TLT), that uses a latent generative
model to estimate robust feature representations from current observational
input for noise image classification. Depending on the estimated noise level
(modeled as a binary treatment factor), TLT assigns the corresponding inference
network trained by the designed causal loss for prediction. We also create new
noisy image datasets incorporating a wide range of noise factors (e.g., object
masking, style transfer, and adversarial perturbation) for performance
benchmarking. The superior performance of TLT in noisy image classification is
further validated by several refutation evaluation metrics. As a by-product,
TLT also improves visual salience methods for perceiving noisy images.
