Latent Variables, Causal Models and Overidentieying Constraints

Latent Variables, Causal Models, and Overidentifying Constraints

Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints

Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score ...

On the Testability of Causal Models With Latent and Instrumental Variables

Certain causal models involving unmea­ sured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality con­ straints on the observed distribution. This paper derives a general formula for such in­ equality constraints as induced by instru­ mental variables, that is, exogenous vari­ ables that directly affect some variables but not all. With th...

Testing Edges by Truncations

We consider the problem of testing whether two variables should be adjacent (either due to a direct effect between them, or due to a hidden common cause) given an observational distribution, and a set of causal assumptions encoded as a causal diagram. In other words, given a set of edges in the diagram known to be true, we are interested in testing whether another edge ought to be in the diagra...

Estimating Causal Effects with Ancestral Graph Markov Models

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...