Counterfactual Learning of Stochastic Policies with Continuous Actions

TMLR Paper2710 Authors

Abstract:

Counterfactual reasoning from logged data has become increasingly important for many
applications such as web advertising or healthcare. In this paper, we address the problem of
learning stochastic policies with continuous actions from the viewpoint of counterfactual risk
minimization (CRM). While the CRM framework is appealing and well studied for discrete
actions, the continuous action case raises new challenges about modelization, optimization,
and offline model selection with real data which turns out to be particularly challenging. Our
paper contributes to these three aspects of the CRM estimation pipeline. First, we introduce
a modelling strategy based on a joint kernel embedding of contexts and actions, which
overcomes the shortcomings of previous discretization approaches. Second, we empirically
show that the optimization aspect of counterfactual learning is important, and we demonstrate
the benefits of proximal point algorithms and smooth estimators. Finally, we propose an
evaluation protocol for offline policies in real-world logged systems, which is challenging since
policies cannot be replayed on test data, and we release a new large-scale dataset along with
multiple synthetic, yet realistic, evaluation setups.

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Marcello_Restelli1

Submission Number: 2710