4 citations · 4 across the 3 of their papers we have counts for
3 papers · 1 filter
What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms
Mikko Väänänen, Fanyu Cui, Carlos Cinelli +1
Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which c…
Pragmatic DML with AI-Learned Representations
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli +5
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is val…
Omitted Variable Bias in Difference-in-Differences Designs
Juejue Wang, Pedro H. C. Sant'Anna, Victor Chernozhukov +1
We study the omitted variable bias (OVB) problem in canonical difference-in-differences (DiD) designs when unobserved confounding induces departures from the parallel trends assump…