Causal inference in observational settings seeks to estimate the effect of exposures, treatments or interventions on outcomes in the absence of random assignment. Unlike experimental designs, ...

This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...

Most AI systems are trained on historical data. When conditions shift due to changing consumer sentiment, models trained on ...

Correlation is not Causation! But how can we find answers to questions like "How effective is a given treatment in preventing a disease" or "Did global warming cause this heat wave" based on available ...

Marketplaces are not clean laboratories. On any large platform, a change rolled out to one group of users ripples across the ...