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…
Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not identifiable from observational data alone. Experi…