Distinguishing between different causal models Seminar

Causal models based upon directed acyclic graphs (DAGs, or Bayesian networks) have gained wide attention over the past 20 years, but accounting for the effect of hidden variables in this context has proved extremely challenging. The resulting marginalized DAG models (mDAGs) fail to display many of the nice properties of ordinary DAGs, and they are difficult to describe mathematically.

We introduce these models and gives some recent results on their characterization. The nested Markov models of Richardson et al (2013) provide approximations to the mDAG models which are much easier to work with; we show that the nested model is 'complete', in that it gives a complete algebraic description of the mDAG. If there is time, we will also discuss some methods for finding inequality constraints in mDAG models, and how these may be used (in principle) to distinguish between different causal hypotheses, even using only observational data.