The development and deployment of machine learning systems can be executed easily with
modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence …

While the traditional viewpoint in machine learning and statistics assumes training and
testing samples come from the same population, practice belies this fiction. One strategy …

Estimating an individual treatment effect (ITE) is essential to personalized decision making.
However, existing methods for estimating the ITE often rely on unconfoundedness, an …

Reducing cycle time is a recurrent concern in the field of business process management.
Depending on the process, various interventions may be triggered to reduce the cycle time …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a
broad class of causal parameters that can be identified as linear functionals of the …

Estimation of causal effects involves crucial assumptions about the data-generating process,
such as directionality of effect, presence of instrumental variables or mediators, and whether …

Assessing sensitivity to unmeasured confounding is an important step in observational
studies, which typically estimate effects under the assumption that all confounders are …

Establishing cause-effect relationships from observational data often relies on untestable
assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from …

Causal inference from observational datasets often relies on measuring and adjusting for
covariates. In practice, measurements of the covariates can often be noisy and/or biased, or …

Measuring human feedback via randomized experimentation is a cornerstone of data-driven
decision-making. The methodology used to estimate user preferences from their online …