In some scenarios, the observational data needed for causal inferences are spread over two files. particular, we consider scenarios where one file includes covariates and treatment measured on a set of individuals, second responses another, partially overlapping individuals. absence error-free direct identifiers like social security numbers, straightforward merging separate files is not feasibl...

Abstract Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet inferences from are especially credible because they depend a known assignment process, not probability model of In this article, I derive randomization-based procedure for effects finite population setting. formally show that satisfies analogues unbiasedness and consist...

We consider a binary response which is potentially affected by set of continuous variables. Of special interest the causal effect on due to an intervention specific variable. The latter can be meaningfully determined basis observational data through suitable assumptions generating mechanism. In particular we assume that joint distribution obeys conditional independencies (Markov properties) inh...

Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions , or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current f...

Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, har...