A central question in many fields of scientific research is to determine how an outcome is
affected by an action, ie, to estimate the causal effect or treatment effect of an action. In …

Computational advertising (CA) is a rapidly growing field, but there are numerous
challenges related to measuring its effectiveness. Some of these are classic challenges …

Individual treatment effect models allow optimizing decision-making by predicting the effect
of a treatment on an outcome of interest for individual instances. These predictions allow …

Causal decision making (CDM) at scale has become a routine part of business, and
increasingly, CDM is based on statistical models and machine learning algorithms …

Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs)
in randomized experiments. Using large-scale randomized experiments on the Facebook …

The goal of causal classification is to identify individuals whose outcome would be positively
changed by a treatment. Examples include targeting advertisements and targeting retention …

This study develops machine learning methods for the data-driven demand estimation and
assortment planning problem by addressing three subproblems, that is, demand forecasting …

Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment
effects for individuals or subgroups in a population. For example, an HTE‐informed …

On-device machine learning enables the lightweight deployment of recommendation
models in local clients, which reduces the burden of the cloud-based recommenders and …

Estimating the effect of mixed treatments is a crucial problem in causal inference. While
previous studies have focused on econometric analysis, few have positioned the mixed …