Author

Carlos Cinelli

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Preprint Oct 2026

Pragmatic DML with AI-Learned Representations

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 valid and develop a practical framework for causal inference with learned representations. For a broad class of estimands, an imperfect represe...

Andrés Fernández, V. Chernozhukov, Carlos Cinelli et al. · 0 citations

Preprint Oct 2026

What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms

Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which can then be used to deduce its logical implications. Yet, in practical applications, model specification is often an iterative process, and i...

Mikko Väänänen, Fan-Yu Cui, Carlos Cinelli et al. · 0 citations

Preprint Sep 2026

Omitted Variable Bias in Difference-in-Differences Designs

We study the omitted variable bias (OVB) problem in canonical difference-in-differences (DiD) designs when unobserved confounding induces departures from the parallel trends assumption. Our results provide a novel characterization of the OVB formula for the average treatment effect on the treated (ATT), which is of ind...

Jue-Jue Wang, Pedro H. C. Sant'Anna, V. Chernozhukov et al. · 0 citations