3 accepted papers
Decision making often depends on causal effect estimation. For example, clinical decisions are often based on estimates of the probability of post-treatment outcomes. Experimental data from randomized controlled trials allow for unbiased estimation of these probabilities. However, such data are usua…
Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model. In this paper, we focus on feature selection for post-intervention outcome prediction from pre-intervention variables. We are motivated by healthcare settings, wher…
The discovery of causal relationships is a core part of scientific research. Accordingly, over the past several decades, algorithms have been developed to discover the causal structure for a system of variables from observational data. Learning ancestral graphs is of particular interest due to their…