Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in
an infinite-horizon decision problem, now refers to a general class of reinforcement learning …

Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …

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 …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

In many areas, practitioners seek to use observational data to learn a treatment assignment
policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or …

This paper investigates the problem of bounding counterfactual queries from an arbitrary
collection of observational and experimental distributions and qualitative knowledge about …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

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 …

The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …