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

Many applications of computational social science aim to infer causal conclusions from non-
experimental data. Such observational data often contains confounders, variables that …

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 …

A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

Abstract recommendation, and marketing services. Extensive efforts have been made to
conquer the cross-modal retrieval problem in the general domain. When it comes to E …

Understanding predictions made by deep neural networks is notoriously difficult, but also
crucial to their dissemination. As all machine learning–based methods, they are as good as …

Text as data techniques offer a great promise: the ability to inductively discover measures
that are useful for testing social science theories with large collections of text. Nearly all text …

Spurious correlations threaten the validity of statistical classifiers. While model accuracy may
appear high when the test data is from the same distribution as the training data, it can …

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic
pretraining, where the pretraining data distribution differs from that of downstream data on …

Controllable text generation concerns two fundamental tasks of wide applications, namely
generating text of given attributes (ie, attribute-conditional generation), and minimally editing …