CILP 2015 - Causal Inference: Learning and Prediction Workshop

Causality is central to how we view and react to the world around us, to our decision making, and to the advancement of science. Causal inference in statistics and machine learning has advanced rapidly in the last 20 years, leading to a plethora of new methods, both for causal structure learning and for making causal predictions (i.e., predicting what happens under interventions). However, a side-effect of the increased sophistication of these approaches is that they have grown apart, rather than together.
The aim of this workshop is to bring together researchers interested in the challenges of causal inference from observational and interventional data, especially when latent (confounding) variables or feedback loops may be present. Contributions describing practical applications of causal methods are specially encouraged. This one-day workshop will explore these topics through a set of invited talks, presentations and a poster session.
Example topics:
Addressing the challenge of practical causal inference in the context of real applications;
Developing measures and methods for evaluating the quality of causal predictions;
Feasible prediction of post-interventional distributions by reconstructing latent confounders;
Considering the relative robustness of assumptions and algorithms to model misspecification;
Methods for causal inference from high-dimensional data;
Methods for combining different datasets;
Experimental design for causal inference;
Real-world validation of causal inference methods;
Discussions on the possibility of making causal predictions in a highly confounded and cyclic world;
Occam’s Razor in causal inference (methodological justifications for oversimplified models).