"What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems

One of the promises of Big Data is its potential to answer “what if?” questions in digital, natural and social systems. Whether we speak of genetic interactions in a cell, passengers commuting in railways and roads, recommender systems matching users to ads, or understanding contagion in social networks, such systems are composed of many interacting components that suggest that learning to control them or understanding the effect of shocks to a system is not an easy task. What if some railways are closed, what will passengers do? What if we incentivize a member of a social network to propagate an idea, how influential can they be? What if some genes in a cell are knocked-out, which phenotypes can we expect?

Such questions need to be addressed via a combination of experimental and observational data, and require a careful approach to modelling heterogeneous datasets and structural assumptions concerning the causal relations among the components of the system. The workshop is aimed at bringing together research expertise from a variety of communities in machine learning, statistics, engineering, and the social, medical and natural sciences. It is an opportunity for methods for causal inference, reinforcement learning and game theory to be cross-fertilized with more traditional research in statistics and the real-world constraints found in practical applications. Ultimately, this can lead to new research platforms to aid the assessment of policies, shocks and experimental design methods in the discovery of breakthroughs in a variety of domains.