Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since experiments can be costly, it is preferable to select interventions that yield maximum amount information about system. We propose novel Bayesian method for optimal experimental design by sequentially selecting minimize expected posterior entropy as rapidly possible. A k...

Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks. We first encode an intervention as changing a conditional probabilistic table into a partial intervention table. We next introduce a jointree algorithm to compu...

Causal discovery is widely used for analysis of experimental data focusing on the exploratory analysis and suggesting probable causal dependencies. There is a variety of causal discovery algorithms in the literature. Some of these algorithms rely on the assumption that there are no latent variables in the model; others do not provide a scoring metric to easily compare the reliability of two can...

Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the faithfulness condition is violated. The constraint-based procedure Boolean satisfiability (SAT) solver, and recently proposed Sparsest Permutation (SP) algorithm shown superb performance...

Causal manipulation theorems proposed by Spirtes et al. in the context of directed probabilistic graphs, such as Bayesian networks, do not model so called reversible causal mechanisms, i.e., mechanisms that are capable of working in several directions, depending on which of their variables are manipulated exogenously. An example involving reversible causal mechanisms is the power train of a car...

In this paper we provide Bayesian matching methods for finding the causal effect of a binary intake variable x ∈ {0, 1} on an outcome of interest y. One technique we introduce is a Bayesian variant of the classic Rosenbaum and Rubin (1983, 1984) propensity score matching method. We show how it is possible to find the posterior distribution of the Bayesian matched sample average treatment effect...

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assu...

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to ...