Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data

no code implementations • NeurIPS 2014 • Karthika Mohan, Judea Pearl

We address the problem of deciding whether a causal or probabilistic query is estimable from data corrupted by missing entries, given a model of missingness process.

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

no code implementations • 25 Nov 2014 • Guy Van den Broeck, Karthika Mohan, Arthur Choi, Judea Pearl

In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network.

Graphical Models for Inference with Missing Data

no code implementations • NeurIPS 2013 • Karthika Mohan, Judea Pearl, Jin Tian

We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random.