… -variable model of visual causation. Using our theory of visual causation we can aggregate
the information present in visual micro-variables (image pixels) into the visual cause C and …

… Causal feature learning is motivated by the need to automate the process of developing
such hierarchical descriptions starting from the less-constrained space of microvariables. The …

… perspective of causal learning for the first time. Thanks to the effective learning of causal
features, our CICF … This validates the effectiveness of our algorithm for causal feature learning. …

… learning from imbalanced data distributions and difficulty in invariant feature learning
from … To address these issues, this paper presents a balanced meta-causal learner (BMCL), …

… Therefore, by using intervention P(Y |do(X)) as the feature learning objective, we can
adjust between “common” and “sense-making”, thus alleviate the observational bias. …

… attention that attends to the desired causal features (eg, foreground)… We analyze how CaaM
learns better causal features than … visual attention module CaaM who learns causal features …

… representation and model learning with good cognitive ability… causal reasoning methods for
visual representation learning, … causal reasoning algorithms in visual representation learning…

… sic causal mechanisms by learning causal … causal factors exactly since they are unobservable.
Therefore, we propose to learn causal representations based on the properties of causal …

… Here, we show the difference between causal convolution and vanilla … -based feature
aggregation scheme to compute AAFs and ANFs. In summary, the CP module targets the learning …

… feature’s membership towards predicting a class P. We define causal features C as those
features … Conversely, the absence of causal features C decreases the probability of decision P. …