Causal Learning of Paired Vectors with Label Noise: Impact and Correction Methods

Oregon Convention Center 

Causal inference involves determining whether a cause-effect relationship exists between two sets of interest, a task that can be framed as a binary classification problem. When dealing with a sequence of independent and identically distributed paired vectors, the kernel mean embedding of the probability distribution can be utilized to map the empirical distribution to a feature space. Subsequently, a classifier is trained in this feature space to predict causation for future vector pairs. However, this approach is susceptible to mislabeling of causal relationships, a common challenge in causation studies. In this talk, I will discuss the impact of mislabeled outputs on the training results. Moreover, I will present robust learning methods that take into account the mislabeling effects and offer theoretical justifications for the validity of these proposed methods.