4 accepted papers
In many real-world settings, such as medicine and finance causal effect is a valuable metric for decision making. For many predictive tasks, causal mechanisms provide robust estimators while existing ML-driven predictors might be vulnerable to spurious correlations. In such settings, when data is de
Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods, which is difficult to estimate for high-dimensional (particu…
Sound and complete algorithms have been proposed to compute identifiable causal queries using the causal structure and data. However, most of these algorithms assume accurate estimation of the data distribution, which is impractical for high-dimensional variables such as images. On the other hand, m…
One fundamental problem in machine learning is out-of-distribution generalization. A method named the surgery estimator incorporates the causal structure in the form of a directed acyclic graph (DAG) to find predictors that are invariant across target domains using distributional invariances via Pea…