Methods for learning causal dependencies from observational data have been the focus of decades of work in social science, statistics, machine learning, and philosophy [9, 10, 11]. Much of the theoretical and practical work on causal discovery has focused on propositional representations. Propositional models effectively represent individual directed causal dependencies (e.g., path analysis, Ba...

The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature (Conditional Independence Assumption). This paper discusses identification using CIA when there are more than two types of mutually exclusive treatments. It turns out that low dimensional balancing scores, similar to the o...

Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods discover causal variables by measuring each variable’s degree of statistical dependence with the target using dependence measures (DMs). However, other methods measure each variable’s ability to explain the statistical ...

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenoty...

Decision makers, such as doctors and judges, make crucial decisions such as recommending treatments to patients, and granting bail to defendants on a daily basis. Such decisions typically involve weighing the potential benefits of taking an action against the costs involved. In this work, we aim to automate this task of learning cost-effective, interpretable and actionable treatment regimes. We...

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditi...

Recent research comparing mental models theory and causal Bayes nets for their ability to account for discounting and augmentation inferences in causal conditional reasoning had some limitations. One of the experiments used an ordinal scale and multiple items and analysed the data by subjects and items. This procedure can create a variety of problems that can be resolved by using an appropriate...

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants' effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biolog...

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenoty...