Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …

We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …

We present a novel unsupervised feature representation learning method, Visual
Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an …

Causal inference from observational data is a vital problem, but it comes with strong
assumptions. Most methods assume that we observe all confounders, variables that affect …

The task of recommender systems is classically framed as a prediction of users' preferences
and users' ratings. However, its spirit is to answer a counterfactual question:“What would the …

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize
their fairness and, in particular, the disparate impacts that ostensibly color-blind algorithms …

Representation learning constructs low-dimensional representations to summarize essential
features of high-dimensional data. This learning problem is often approached by describing …

The estimation of treatment effects is a pervasive problem in medicine. Existing methods for
estimating treatment effects from longitudinal observational data assume that there are no …

A fundamental challenge in observational causal inference is that assumptions about
unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is …