Applying causal inference models in areas such as economics, healthcare and marketing
receives great interest from the machine learning community. In particular, estimating the …

Applying causal methods to fields such as healthcare, marketing, and economics receives
increasing interest. In particular, optimising the individual-treatment-effect–often referred to …

In this tech report we discuss the evaluation problem of contextual uplift modeling from the
causal inference point of view. More particularly, we instantiate the individual treatment …

Abstract Off-Policy Evaluation (OPE) in contextual bandits is crucial for assessing new
policies using existing data without costly experimentation. However, current OPE methods …

Individual Treatment Effect (ITE) prediction is an important area of research in machine
learning which aims at explaining and estimating the causal impact of an action at the …

This work explores the idea of a causal contextual multi-armed bandit approach to
automated marketing, where we estimate and optimize the causal (incremental) effects …

Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and
thus require the analyst to specify a fixed sample size in advance. However, in many online …

Evaluating novel contextual bandit policies using logged data is crucial in applications
where exploration is costly, such as medicine. But it usually relies on the assumption of no …

Excessively changing policies in many real world scenarios is difficult, unethical, or
expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so …

When learning from a batch of logged bandit feedback, the discrepancy between the policy
to be learned and the off-policy training data imposes statistical and computational …