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stat.ME2026
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…
stat.ME2026
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…