Proposed in late May, the rule would give political appointees unprecedented control over federal grants for research, education and infrastructure.

Stand up for Science

New Trump administration rules would undermine longstanding research practices. It’s death by a thousand cuts

2026 Penn Causal Inference Summer Institute

Riders at the dreary rail hub said they just wanted to see transit improvements continue.

More than a hundred protesters and observers have gathered near E. 26th Street and Nicollet Avenue.

Missing data has the potential to affect analyses conducted in all fields of scientific study including healthcare, economics, and the social sciences. Several approaches to unbiased inference in t...

As Latino communities ramp up air quality monitoring, Trump’s EPA moves to weaken pollution regulations

After 100 days in immigration detention, journalist Mario Guevara shares a handwritten letter describing the toll of separation from his family, his love for the U.S., and his determination to keep re...

From the firing of the labor statistics chief to plans for a new census, the president’s moves serve to entrench authoritarianism

The study of disparities in the liver transplantation process may focus on quantifying causal effects, particularly the average, direct, or indirect effects of various social determinants of health on...

It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every...

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The administration is attacking research, health and the environment. We might seem unlikely activists – but we have a duty to dissent

We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in case-control studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a node for the sampling indicator, assumptions about sampling processes can be made explicit. We demonstrate how to read off such graphs whether consistent estimation of the association between exposure and outcome is possible. Moreover, we give sufficient graphical conditions for testing and estimating the causal effect of exposure on outcome. The practical use is illustrated with a number of examples.

The administration is attacking research, health and the environment. We might seem unlikely activists – but we have a duty to dissent