Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

As the class size grows, maintaining a balanced dataset across many classes is challenging
because the data are long-tailed in nature; it is even impossible when the sample-of-interest …

Today's scene graph generation (SGG) task is still far from practical, mainly due to the
severe training bias, eg, collapsing diverse" human walk on/sit on/lay on beach" into" human …

Lagged explanatory variables are commonly used in political science in response to
endogeneity concerns in observational data. There exist surprisingly few formal analyses or …

Previous works for ObjectNav task attempt to learn the association (eg relation graph)
between the visual inputs and the goal during training. Such association contains the prior …

Do voters polarize ideologically when radical views gain political legitimacy, or does the rise
of radical voices merely reflect societal conflict? We argue that elite polarization as signaled …

A common causal identification strategy in political science is selection on observables. This
strategy assumes one observes a set of covariates that is, after statistical adjustment …

Named entity recognition (NER) is a fundamental task to recognize specific types of entities
from a given sentence. Depending on how the entities appear in the sentence, it can be …

Quantitative analysis of causal effects in political science has trended toward the adoption of
“causal empiricist” approaches. Such approaches place heavy emphasis on causal …

Causal inference lies at the core of many scientific endeavours. Yet, answering causal
questions is challenging, especially when studying culture as a causal force. Against this …