A Strategy for Making Predictions Under Manipulation

Laura E. Brown and Ioannis Tsamardinos; JMLR W&CP 3:35-52, 2008.

Abstract

The first Causality Challenge competition posted several causal discovery problems that require researchers to employ the full arsenal of state-of-the-art causal discovery methods, while prompting the development of new ones. Our approach used the formalism of Causal Bayesian Networks to model and induce causal relations and to make predictions about the effects of the manipulation of the variables. Using state-of-the-art, under development, or newly invented methods specifically for the purposes of the competition, we addressed the following problems in turn in order to build and evaluate a model: (a) finding the Markov Blanket of the target even under some non-faithfulness conditions (e.g., parity functions), (b) reducing the problems to a size manageable by subsequent algorithms, (c) identifying and orienting the network edges, (d) identifying causal edges (i.e., not confounded), and (e) selecting the causal Markov Blanket of the target in the manipulated distribution. The results of the competition illustrate some of the strengths and weaknesses of the state-of-the-art of causal discovery methods and point to new directions in the field. An implementation of our approach is available at http://www.dsl-lab.org for use by other researchers.