We address the problem of causal discovery in the two-variable case given a sample from their joint distribution. The proposed method is based on a known assumption that, if X → Y (X causes Y ), the marginal distribution of the cause, P (X), contains no information about the conditional distribution P (Y |X). Consequently, estimating P (Y |X) from P (X) should not be possible. However, estimati...

This paper concerns the assessment of direct causal effects from a combination of: (i) non­ experimental data, and (ii) qualitative do­ main knowledge. Domain knowledge is en­ coded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Var...

This paper demonstrates the identification of causal mechanisms in experiments with a binary treatment, (primarily) based on inverse probability weighting. I.e., we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the (unmediated) direct effec...

We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore ...

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram g...

A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that 8-month-old infants registered conditional indepen...

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at lea...

Various relationships are shown hold between monotonic effects and weak monotonic effects and the monotonicity of certain conditional expectations. Counterexamples are provided to show that the results do not hold under less restrictive conditions. Monotonic effects are furthermore used to relate signed edges on a causal directed acyclic graph to qualitative effect modification. The theory is a...

Modern causal inference owes much of its progress to a strict and crisp distinction between probabilistic and causal information. This distinction recognizes that probability theory is insufficient for posing causal questions, let alone answering them, and dictates that every exercise in causal inference must commence with some extra knowledge that cannot be expressed in probability alone. The ...

In this paper we provide a formal account of how information about causal processes (i.e., knowledge of the causal chain linking an explanatory variable to an outcome variable) can be used to sharpen causal inferences. All of this is done within a Bayesian potential outcomes causal model. The methods discussed in this paper empower researchers by providing them with a richer palette of causal a...