One of the fundamental challenges found throughout the data sciences is ...
As society transitions towards an AI-based decision-making infrastructur...
Since the rise of fair machine learning as a critical field of inquiry, ...
We study causal representation learning, the task of inferring latent ca...
Identifying the effects of new interventions from data is a significant
...
Evaluating hypothetical statements about how the world would be had a
di...
"Monkey see monkey do" is an age-old adage, referring to naïve imitation...
One of the common ways children learn is by mimicking adults. Imitation
...
Decision-making systems based on AI and machine learning have been used
...
Visual representations underlie object recognition tasks, but they often...
One pervasive task found throughout the empirical sciences is to determi...
This paper investigates the problem of bounding counterfactual queries f...
The Ladder of Causation describes three qualitatively different types of...
One of the central elements of any causal inference is an object called
...
One of the most common mistakes made when performing data analysis is
at...
Assessing the magnitude of cause-and-effect relations is one of the cent...
We study the problem of causal structure learning when the experimenter ...
In this paper, we extend graph-based identification methods by allowing
...
The generalizability of empirical findings to new environments, settings...
Generalizing empirical findings to new environments, settings, or popula...
We address the problem of estimating the effect of intervening on a set ...